DePaul University

About: DePaul University is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 5658 authors who have published 11562 publications receiving 295257 citations.

Does Trust Matter? The Relationship Between Equity Sensitivity and Perceived Organizational Justice

Abstract: The present research study was designed to extend our knowledge about issues of relevance for business ethics by examining the role of equity sensitivity and perceived organizational trust on employees’ perceptions of procedural and interactional justice. A model was developed and tested, and results revealed that organizational trust and respect mediated the relationship between an employee’s equity sensitivity and perceptions of procedural, interactional, and social accounts fairness. A discussion of issues related to perceptions of trust and fairness is presented, as well as recommendations for leaders and future scholarship.

A Conceptual Framework for the Long-Term Psychological Effects of Traumatic Childhood Abuse

Abstract: Although trauma is not an intrinsic part of all child abuse, the impact of some abuse may be best conceptualized from a traumatic stress perspective. The authors first propose three critical elements that make some abuse traumatic. They then describe the response at the time of trauma and the processes that lead to persistence of that response. Reexperiencing and avoidance are proposed as the core trauma responses, and six secondary and associated symptoms are also described. In addition, the influences on long-term adjustment of biological factors, developmental level at the time of abuse, severity of abuse, social context, and prior and subsequent life events are discussed. The authors' conceptual framework is applied to make specific predictions about the relationships between particular abuse characteristics and later symptoms, and research on adults with histories of child abuse and on abused children is reviewed for consistency with these predictions.

A theory of memory models

Abstract: A memory model for a concurrent imperative programming language specifies which writes to shared variables may be seen by reads performed by other threads. We present a simple mathematical framework for relaxed memory models for programming languages. To instantiate this framework for a specific language, the designer must choose the notion of atomic steps supported by the language (e.g. 32-bit reads and writes) and specify how a composite step may be broken into a sequence of atomic steps (the decomposition rule). This rule determines which sequence of intermediate writes (if any) are visible to concurrent reads by other threads. Different choices of the rule lead to models which permit a read to return any value if there is a concurrent write (race), or models which satisfy a "No Thin Air Read"property. The former is suitable for languages such as C++(programs with races have undefined behavior), and the latter for Java. Other intermediate models are possible, useful and interesting.We establish that all models in the framework satisfy the Fundamental Property of relaxed memory models: programs whose sequentially consistent (SC) executions have no races must have have only SC executions. We show how to define synchronization constructs (such as volatiles of various kinds) in the framework, and discuss the causality test cases from the Java Memory Model.

Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems

Abstract: Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers who cannot be readily distinguished from ordinary users may introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. A handful of simple attack models have, so far, been identified, and there appear to be significant differences in the susceptibility of different recommendation techniques to these attacks. In particular, item-based collaborative filtering has been found to offer some security advantages over user-based collaborative filtering. Our research in secure personalization is examining a range of more complex attack models and recommendation techniques, paying particular attention to the costs and benefits of mounting an attack. In this paper, we take a closer look at item-based collaborative filtering. In particular, we propose a new attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against an item-based algorithm.