A combined control strategy using tuned liquid dampers to reduce displacement demands of base-isolated structures: a probabilistic approach

This paper investigates a hybrid structural control system using tuned liquid dampers (TLDs) and lead-rubber bearing (LRB) systems for mitigating earthquake-induced vibrations. Furthermore, a new approach for taking into account the uncertainties associated with the steel shear buildings is proposed. In the proposed approach, the probabilistic distributions of the stiffness and yield properties of stories of a set of reference steel moment frame structures are derived through Monte-Carlo sampling. The approach is applied to steel shear buildings isolated with LRB systems. The base isolation systems are designed for different target base displacements by minimizing a relative performance index using Genetic Algorithm. Thereafter, the base-isolated structures are equipped with TLDs and a combination of the base and TLD properties is sought by which the maximum reduction occurs in the base displacement without compromising the performance of the system. In addition, the effects of TLD properties on the performance of the system are studied through a parametric study. Based on the analyses results, the base displacement can be reduced 23% by average, however, the maximum reduction can go beyond 30%.     

Blind evaluation of location based queries using space transformation to preserve location privacy

In this paper we propose a fundamental approach to perform the class of Range and Nearest Neighbor (NN) queries, the core class of spatial queries used in location-based services, without revealing any location information about the query in order to preserve users’ private location information. The idea behind our approach is to utilize the power of one-way transformations to map the space of all objects and queries to another space and resolve spatial queries blindly in the transformed space. Traditional encryption based techniques, solutions based on the theory of private information retrieval, or the recently proposed anonymity and cloaking based approaches cannot provide stringent privacy guarantees without incurring costly computation and/or communication overhead. In contrast, we propose efficient algorithms to evaluate KNN and range queries privately in the Hilbert transformed space. We also propose a dual curve query resolution technique which further reduces the costs of performing range and KNN queries using a single Hilbert curve. We experimentally evaluate the performance of our proposed range and KNN query processing techniques and verify the strong level of privacy achieved with acceptable computation and communication overhead.     

Users plan optimization for participatory urban texture documentation

We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high quality urban texture documentation. We propose to implement PTD in two steps. At the first step, termed viewpoint selection, a minimum number of viewpoints in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) with a desirable quality can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we define each of these steps and prove that both are NP-hard problems. Accordingly, we propose efficient algorithms to implement the viewpoint selection and assignment problems. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.     

Location privacy: going beyond K-anonymity,cloaking and anonymizers

With many location-based services, it is implicitly assumed that the location server receives actual users locations to respond to their spatial queries. Consequently, information customized to their locations, such as nearest points of interest can be provided. However, there is a major privacy concern over sharing such sensitive information with potentially malicious servers, jeopardizing users’ private information. The anonymity- and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they require a trusted intermediate anonymizer to protect user locations during query processing. This paper proposes a fundamental approach based on private information retrieval to process range and K-nearest neighbor queries, the prevalent queries used in many location-based services, with stronger privacy guarantees compared to those of the cloaking and anonymity approaches. We performed extensive experiments on both real-world and synthetic datasets to confirm the effectiveness of our approaches.