The effect of bilingual representation in prose reading on explicit and implicit memory

Chinese-English bilingual students were randomly assigned to three reading conditions: In the English-English (E-E) condition (n = 44), a text in English was read twice; in the English-Chinese (E-C) condition (n = 30), the English text was read first and its Chinese translation was read second; in the Chinese-English (C-E) condition (n = 30), the Chinese text was read first and English second. An expected explicit memory test on propositions in the format of sentence verification was given followed by an unexpected implicit memory test on unfamiliar word-forms.^ Analyses of covariance were conducted with explicit and implicit memory scores as the dependent variables, reading condition (bilingual versus monolingual) as the independent variable, and TOEFL reading score as the covariate.^ The results showed that the bilingual reading groups outperformed the monolingual reading group on explicit memory tested by sentence-verification but not on implicit memory tested by forced-choice word-identification, implying that bilingual representation facilitates explicit memory of propositional information but not implicit memory of lexical forms. The findings were interpreted as consistent with separate bilingual memory-storage models and the implications of such models in the study of cognitive structures were discussed in relationship to issues of dual coding theory, multiple memory systems, and the linguistic relativity philosophy. ^

Memory conformity: Actors and bystanders

This dissertation explored memory conformity effects on people who interacted with a confederate and of bystanders to that interaction. Two studies were carried out. Study 1 was conducted in the field. A male confederate approached a group of people at the beach and had a brief interaction. About a minute later a research assistant approached the group and administered a target-absent lineup to each person in the group. Analyses revealed that memory conformity occurred during the lineup task. Bystanders were twice as likely to conform as those who interacted with the confederate. Study 2 was carried out in a laboratory under controlled conditions. Participants were exposed to two events during their time in the laboratory. In one event, participants were shown a brief video with no determinate roles assigned. In the other event participants were randomly assigned to interact with a confederate (actor condition) or to witness that interaction (bystander condition). Participants were given memory tests on both events to understand the effects of participant role (actor vs. bystander) on memory conformity. Participants answered second to all questions, following a confederate acting as a participant, who disseminated misinformation on critical questions. Analyses revealed no significant differences in memory conformity between actors and bystanders during the movie memory task. However, differences were found for the interaction memory task such that bystanders conformed more than actors on two of four critical questions. Bystanders also conformed more than actors during a lineup identification task. The results of these studies suggest that the role a person plays in an interaction affects how susceptible they are to information from a co-witness. Theoretical and applied implications are discussed. First, the results are explained through the use of two models of memory. Second, recommendations are made for forensic investigators.

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.