Policing and Homelessness

This research examines the barriers that exist between law enforcement and the homeless population of Lawrence, Kansas. Based on the criminalization of certain homeless practices and the treatment of homeless people by government officials, persons experiencing homelessness are less likely to report crimes to the proper authorities. Being the case, law enforcement methodology must change to a community-oriented policing style in order to create open lines of communication between law enforcers and this unique community. This study examined the homeless population\u27s hesitancy to contact the police when they were in need of assistance and compared the results to both how they had been treated by law enforcement in the past and their knowledge of community-oriented policing. The study ultimately yielded mixed results on both contacting the police and past procedurally just treatment by law enforcement. Furthermore, not a single respondent was able to accurately identify community-oriented policing. The research led to the conclusion that although some homeless people in Lawrence, Kansas experienced fair treatment, the barriers between law enforcement and the homeless population could be effectively overcome by implementing true community-oriented policing

A metrics paradigm for object-oriented analysis and design.

For several years, the software industry has been affected by a problem called the software crisis. Many software developers have worked hard to determine remedies for this crisis. The crisis deals with products being produced over costs, behind schedule, with low quality, and not meeting customer satisfaction. Processes are being used which waste resources and build low quality products. In dealing with the software crisis, software practitioners have used many tools, methodologies, and metrics to help produce better products, save resources, and increase productivity. Metrics are meaningful measures used to determine how well software is being produced, where weaknesses are in products, processes, or resources, and indicate where to make improvements. Today, the most popular methodology is the Object-Oriented (00) methodology. Users of this methodology want to prove that it helps resolve the software crisis. There is a belief that 00 systems are developed earlier, easier to understand and maintain, and contain reusable components. However, without metrics, the effectiveness of the 00 approach cannot be proven. Metrics must be used to show if the process or resources being used is responsible for the success or failure of software development, if management of the process is responsible, if the complexity or size of the product being produced is responsible, or if a lack of communication or misunderstandings of requirements is responsible. There are many, many reasons why metrics are used and can be seen in this Thesis. Many metrics are being used for traditional software development methodologies that deal with processes, products, and resources. However, there is no metric paradigm for the 00 development methodology. But, there is a small set of metrics proposed for 00 programming. We provide a 00 metric paradigm that contains metrics grouped into nine categories. We grouped the metrics into categories to make the selection of a particular· metric easier. These nine metrics fit nicely into the 00 methodology. The metrics can be used to measure processes, products, and resources. We provide a size estimation method that helps determine progress, costs, quality, effort, complexity, and schedule. The metrics can be used to prove the effectiveness of the 00 methodology and indicate areas for further improvement

THE PRESENTATION AND WITHDRAWAL OF POSITIVE REINFORCEMENT AS A BEHAVIORAL MODIFIER OF HYPERACTIVITY

Operant conditioning had its origin in research with infra-humans and later, when humans were used as subjects, the research was still done in a laboratory. Only within the last half of a decade has it been applied with any frequency to humans outside the laboratory

The Other Side of the White Picket Fence

[Excerpt] At the risk of heaping more misery on the struggling residential property market, an analysis of home price and ownership data for period of the last 30 years in California - the golden state with notoriously golden property prices – shows that the average single family house has not been a particularly stellar investment. Over that period, a typical homebuyer would have been wise to learn a thing or two about disciplined investing, and would have been better advised to consider putting his or her money elsewhere if the goals were to build net worth and financial security. The popular misconception that houses are always great investments has wider economic implications as well, as our society becomes increasingly concerned with providing generally for retirement security and housing affordability. Excessive public policy emphasis on owneroccupied housing for social objectives, and reliance on homebuilding as a provider of short-term economic stimulus, may also cause capital to be diverted from more productive investments, and misallocate increasingly scarce public resources. Even if the recent crash is ignored, a house, despite our government’s herculean efforts to give all of us a chance to buy one, may not have been the greatest place to put one’s hard earned money

PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts

Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources months before proper classification into structured databases. In order to facilitate timely discovery of such knowledge, we propose a novel semi-supervised learning algorithm, PACE, for identifying and classifying relevant entities in text sources. The main contribution of this paper is an enhancement of the traditional bootstrapping method for entity extraction by employing a time-memory trade-off that simultaneously circumvents a costly corpus search while strengthening pattern nomination, which should increase accuracy. An implementation in the cyber-security domain is discussed as well as challenges to Natural Language Processing imposed by the security domain.Comment: 6 pages, 3 figures, ieeeTran conference. International Conference on Machine Learning and Applications 201