Bowling Green State University

About: Bowling Green State University is a education organization based out in Bowling Green, Ohio, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8315 authors who have published 16042 publications receiving 482564 citations. The organization is also known as: BGSU.

D-Optimality for Regression Designs: A Review

Abstract: After stating the model and the design problem, we briefly present the results for regression design prior to the work of Kiefer and Wolfowitz. We then review the major results of Kiefer and Wolfowitz, particularly those on the theory of design, as well as the way the criterion has been extended to non-linear models. Finally, we discuss algorithms for constructing D-optimum designs.

Adolescent Well-Being in Cohabiting, Married, and Single-Parent Families

Abstract: Cohabitation is a family form that increasingly includes children. We use the National Longitudinal Study of Adolescent Health to assess the well-being of adolescents in cohabiting parent stepfamilies (N= 13,231). Teens living with cohabiting stepparents often fare worse than teens living with two biological married parents. Adolescents living in cohabiting stepfamilies experience greater disadvantage than teens living in married stepfamilies. Most of these differences, however, are explained by socioeconomic circumstances. Teenagers living with single unmarried mothers are similar to teens living with cohabiting stepparents; exceptions include greater delinquency and lower grade point averages experienced by teens living with cohabiting stepparents. Yet mother's marital history explains these differences. Our results contribute to our understanding of cohabitation and debates about the importance of marriage for children.

Deep Ground Truth Analysis of Current Android Malware

Abstract: To build effective malware analysis techniques and to evaluate new detection tools, up-to-date datasets reflecting the current Android malware landscape are essential For such datasets to be maximally useful, they need to contain reliable and complete information on malware’s behaviors and techniques used in the malicious activities Such a dataset shall also provide a comprehensive coverage of a large number of types of malware The Android Malware Genome created circa 2011 has been the only well-labeled and widely studied dataset the research community had easy access to (As of 12/21/2015 the Genome authors have stopped supporting the dataset sharing due to resource limitation) But not only is it outdated and no longer represents the current Android malware landscape, it also does not provide as detailed information on malware’s behaviors as needed for research Thus it is urgent to create a high-quality dataset for Android malware While existing information sources such as VirusTotal are useful, to obtain the accurate and detailed information for malware behaviors, deep manual analysis is indispensable In this work we present our approach to preparing a large Android malware dataset for the research community We leverage existing anti-virus scan results and automation techniques in categorizing our large dataset (containing 24,650 malware app samples) into 135 varieties (based on malware behavioral semantics) which belong to 71 malware families For each variety, we select three samples as representatives, for a total of 405 malware samples, to conduct in-depth manual analysis Based on the manual analysis result we generate detailed descriptions of each malware variety’s behaviors and include them in our dataset We also report our observations on the current landscape of Android malware as depicted in the dataset Furthermore, we present detailed documentation of the process used in creating the dataset, including the guidelines for the manual analysis We make our Android malware dataset available to the research community