12547630
Performance Evaluation of a Privacy-Enhancing Framework for Personalized Websites
Reconciling personalization with privacy has been a continuing interest in the user modeling community. In prior work, we proposed a dynamic privacy-enhancing user modeling framework based on a software product line architecture (PLA). Our system dynamically selects personalization methods during runtime that respect users' current privacy preferences as well as the prevailing privacy laws and regulations. One major concern about our approach is its performance since dynamic architectural reconfiguration during runtime is usually resource-intensive. In this paper, we describe four implementations of our system that vary two factors, and an in-depth performance evaluation thereof under realistic workload conditions. Our study shows that a customized version performs better than the original PLA implementation, that a multi-level caching mechanism improves both versions, and that the customized version with caching performs best. The average handling time per user session is less than 0.2 seconds for all versions except the original PLA implementation. Overall, our results demonstrate that with a reasonable number of networked hosts in a cloud computing environment, an internationally operating website can use our dynamic PLA-based user modeling approach to personalize their user services, and at the same time respect the individual privacy desires of their users as well as the privacy norms that may apply.
269891
Respecting Users' Individual Privacy Constraints in Web Personalization
Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits may be severely counter acted by privacy constraints. Personalized systems need to take users' privacy concerns into account, as well as privacy laws and industry self-regulation that may be in effect. In this paper, we first discuss how these constraints may affect web-based personalized systems. We then explain in what way current approaches to this problem fall short of their aims, specifically regarding the need to tailor privacy to the constraints of each individual user. We present a dynamic privacy-enhancing user modeling framework as a superior alternative, which is based on a software product line architecture. Our system dynamically selects personalization methods during runtime that respect users' current privacy concerns as well as the privacy laws and regulations that apply to them.
2369938
Human-Currency Interaction: learning from virtual currency use in China
What happens when the domains of HCI design and money intersect? This paper presents analyses from an ethnographic study of virtual currency use in China to discuss implications for game design, and HCI design more broadly. We found that how virtual currency is perceived, obtained, and spent can critically shape gamers' behavior and experience. Virtual and real currencies can interact in complex ways that promote, extend, and/or interfere with the value and character of game worlds. Bringing money into HCI design heightens existing issues of realness, trust, and fairness, and thus presents new challenges and opportunities for user experience innovation.
1173197
Situating Productive Play: Online Gaming Practices and Guanxi in China
Economic activities in and around online gaming in China are often correlated in the West with practices of gold farming, or selling in-game currency to players for real money in online games. What can we learn about online gaming in China and about online gaming and online sociality more broadly when we look at economic and other "pragmatic" practices through which online gaming becomes meaningful to players? In this paper, we present findings from an ethnographic study of online gaming in China's urban Internet cafes to discuss implications for game design, and HCI design more broadly. Considering the ties between socio-economic practices, development of trust and culturally situated imaginings of self-hood and otherness, brings to the fore how online gaming in and of itself constitutes the means for practical achievements in day-to-day management of guanxi (social connection).
1683130
PLA-based runtime dynamism in support of privacy-enhanced Web personalization
Software product line architectures (PLAs) have been widely recognized as a successful approach in industrial software development for improving productivity, software quality and time-to-market. In this paper, we focus on the usage of a PLA for a quite different purpose, namely, handling privacy constraints in Web personalization. To provide personalized services such as customized recommendations, a personalized Website collects users' personal data, which raises various privacy concerns. We aim at reconciling the benefits of web personalization with privacy constraints that come from users themselves as well as from privacy legislations and regulations that apply to a given user. We propose a dynamic, privacy-enabling personalization infrastructure and conceive it as a PLA. This infrastructure allows for dynamically selecting and instantiating personalization architectures that provide personalized services to each individual user and comply with the prevailing privacy constraints
14397325
Constraint-Sensitive Privacy Management for Personalized Web-Based Systems
This research aims at reconciling web personalization with privacy constraints imposed by legal restrictions and by users' privacy preferences. We propose a software product line architecture approach, where our privacyenabling user modeling architecture can dynamically select personalization methods that satisfy current privacy constraints to provide personalization services. A feasibility study is being carried out with the support of an existing user modeling server and a software architecture based development environment.
1772294
Scalable real-time system design using preemption thresholds
The maturity of schedulability analysis techniques for fixed-priority preemptive scheduling has enabled the consideration of timing issues at design time using a specification of the tasking architecture and estimates of execution times for tasks. While successful, this approach has limitations since the preemptive multi-tasking model does not scale well for a large number of tasks, and the fixed-priority scheduling theory does not work well with many object-oriented design methods. In this paper, we present an approach that scales well even when the design consists of a large number of concurrent jobs. The approach avoids any unnecessary preemptability in the system, thereby resulting in reduced run-time overheads from preemptions and associated context switches. It also allows significant memory savings by grouping jobs into non-preemptive groups and then sharing the stack space between them. Our approach is based on our earlier work on scheduling using preemption thresholds that allows parametric control over preemptability in a priority-based system. We show that our approach provides significant advantages over one using a fixed-priority preemptive scheduling architecture. The benefits include higher schedulability for small numbers of tasks and lower run-time overheads, and hence better scalability. We develop algorithms that allow design-time consideration of schedulability and automatic synthesis of an implementation model to minimize run-time overheads.
35749769
EFSM-based Testing Strategy for APIs Test of Embedded OS
In this paper, we present a two-phase approach to generate test data for the EFSM model of an embedded operating system. We first build the EFSM model of the system. The model can then be used to automatically generate test data for testing of APIs. We also point out certain issues that need further study.
15686021
Research on Vector-Control System of PMSM Based on Internal Model Control of Current Loop
The current regulation based on the internal model control is applied to vector control of PMSM. It avoids the adjustment of two PI-parameters; moreover the IMC structure improves the characteristic of the current loop. Generally, the flux of PMSM is changing with the load current (torque). Simulation results under flux saturation considered and not considered show that the current internal model controller can provide good performance with matched model and unmatched model. And the system has good features of the robustness to disturbances, and parameter variations. Satisfactory performance can be achieved.
14073504
A multinomial logistic regression modeling approach for anomaly intrusion detection
Although researchers have long studied using statistical modeling techniques to detect anomaly intrusion and profile user behavior, the feasibility of applying multinomial logistic regression modeling to predict multi-attack types has not been addressed, and the risk factors associated with individual major attacks remain unclear. To address the gaps, this study used the KDD-cup 1999 data and bootstrap simulation method to fit 3000 multinomial logistic regression models with the most frequent attack types (probe, DoS, U2R, and R2L) as an unordered independent variable, and identified 13 risk factors that are statistically significantly associated with these attacks. These risk factors were then used to construct a final multinomial model that had an ROC area of 0.99 for detecting abnormal events. Compared with the top KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm, the multinomial logistic model-based classification results had similar sensitivity values in detecting normal (98.3% vs. 99.5%), probe (85.6% vs. 83.3%), and DoS (97.2% vs. 97.1%); remarkably high sensitivity in U2R (25.9% vs. 13.2%) and R2L (11.2% vs. 8.4%); and a significantly lower overall misclassification rate (18.9% vs. 35.7%). The study emphasizes that the multinomial logistic regression modeling technique with the 13 risk factors provides a robust approach to detect anomaly intrusion.
882072
Scalable, reliable marshalling and organization of distributed large scale data onto enterprise storage environments
Emerging technologies in high speed NAS, hierarchical storage management systems, and networked systems that virtualize interconnected storage over IP and fiber-channel networks, promise to consolidate distributed data stores onto large-scale professionally managed enterprise storage environments. We describe the software architecture of the PAWN (producer-archive workflow network) environment that enables scalable, reliable marshalling and organization of distributed data into such enterprise storage environments. PAWN was initially developed to capture the core elements required for long term preservation of digital objects as identified by researchers in the digital library and archiving communities. In this paper, we show how PAWN can be extended to enable multiple clients at a number of distributed sites to prepare, organize, and bulk transfer large scale data onto clusters of servers that securely verify the integrity of the data, register the metadata, and store the data into an enterprise storage environment. PAWN allows detailed description, auditing, and organization of the data, and hence allows for efficient management, access, and disaster recovery. The basic software components are based on open standards and Web technologies, and hence are platform independent.