7761771
Access control in peer-to-peer collaborative systems
As an emerging model of communication and computation, peer-to-peer networking represents a fully distributed, cooperative network design, and has recently gained significant acceptance. Peer groups share the properties of peer-to-peer overlay network, including full decentralization, symmetric abilities, and dynamism, which make security problems more complicated. In this paper, we propose a fine-grained and attribute-based access control framework for peer-to-peer systems. This design employs a novel policy model, which extends role-based trust management language RT to satisfy security requirements of peer groups. Intend for a pure decentralized model without centralized server, our framework presents distributed delegation authorization mechanism, which could avoid single point of failure. We also introduce our implementation experience.
6412815
Automated task distribution in multicore network processors using statistical analysis
Chip multiprocessor designs are the most common types of architectures seen in Network Processors. As the Network Processors are used to implement increasingly complicated applications, task distribution among the cores is becoming an important problem. In this paper, we propose a new task allocation scheme for such architectures. This scheme relies on the inherent modular nature of the networking applications and intelligently distributes modules among different execution cores. Additionally, we selectively replicate modules to parallelize execution of tasks having longer processing time. We have developed a technique that uses the probability distribution of the execution times of different modules in the networking applications. The proposed schemes result in resource utilization of up to 95%, 89%, and 84% on average for the processors with 2, 4, and 8 cores, respectively. The schemes are highly scalable and can improve the throughput by 6.72 times for 8 core processors, aggregated over four representative applications. The combination of selective replication of modules and variation-aware task allocation result in up to 12.5% (9.9% on average) performance improvement as compared to a scheme based on just mean processing time.
10896727
Firefly: illuminating future network-on-chip with nanophotonics
Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical network architecture. Firefly consists of clusters of nodes that are connected using conventional, electrical signaling while the inter-cluster communication is done using nanophotonics - exploiting the benefits of electrical signaling for short, local communication while nanophotonics is used only for global communication to realize an efficient on-chip network. Crossbar architecture is used for inter-cluster communication. However, to avoid global arbitration, the crossbar is partitioned into multiple, logical crossbars and their arbitration is localized. Our evaluations show that Firefly improves the performance by up to 57% compared to an all-electrical concentrated mesh (CMESH) topology on adversarial traffic patterns and up to 54% compared to an all-optical crossbar (OP XBAR) on traffic patterns with locality. If the energy-delay-product is compared, Firefly improves the efficiency of the on-chip network by up to 51% and 38% compared to CMESH and OP XBAR, respectively.
6059000
Preserving User Location Privacy in Mobile Data Management Infrastructures
Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user's location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.
14869838
A Social Network-Based Trust Model for the Semantic Web
Trust is very essential to secure and high quality interactions on the Semantic Web. In this paper, we introduce a trust model, which incorporates pairwise trust ratings and reliable factors of acquaintances and constructs an edge-weighted graph to calculate trust values. We interpret trust in two dimensions to better deal with it. We apply the theory in social network field to calculate trust and simulate the trust relationships between humans. We also take advantage of formulas in probability and statistics to analyze the similarities between agents. The algorithm of the trust model aims to be simple, efficient and flexible.
58833855
EDFCES: a new example-driven 3D face construction and editing system
This paper presents an automatic runtime system for generating varied, realistic face models by synthesizing a global face shape and local facial features according to intuitive, high-level control parameters. Our method takes as examples 3D face scans in order to exploit the parameter-to-geometry correlations present in real faces. In order to establish the correspondences among the scanned models, we use a three-step model fitting approach to conform a generic head mesh onto each scanned model. We transform the obtained data sets of global face shapes and local feature shapes into vector space representations by applying a principal component analysis (PCA). We compute a set of face anthropometric measurements to parameterize the exemplary shapes in the measurement spaces. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions designed to generate the desired face shape by taking anthropometric parameters as input. At runtime, the interpolation functions are evaluated for the input parameter values to produce new face geometries at an interactive rate. The correspondence among all exemplary face textures is obtained by parameterizing the 3D generic mesh over a 2D image domain. The new feature texture with the desired attributes is synthesized by interpolating the example textures. The resulting system is intuitive to control and fine-grained. We demonstrate our method by applying different parameters to generate a wide range of face models.
39711298
Data-Driven Feature Control Models for Creating Virtual Faces
This paper presents a novel data-driven method for creating realistic face models based on estimated high-level feature control models. Our method takes as examples 3D face scans. By bringing scanned models into full correspondence with a model fitting approach, we apply principal component analysis (PCA) to the exemplar shapes of each facial feature to build a shape space. We compute a set of face anthropometric measurements to parameterize example feature shapes in the measurement spaces. Using the PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar textures is obtained by parameterizing a 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the example textures.
12804860
A Novel 3-DOF HIP Exoskeleton for Kinematically Establishing Animal Model of Avascular Necrosis of the Femoral Head
This paper outlines a detailed description of the design on a 3-DOF hip robotic device for kinematically establishing animal model of avascular necrosis (AVN) of the femoral head. In order to realize the hip extension/flexion and abduction/adduction movements of rabbit subjects, a novel 2-DOF hip motion apparatus based on a pair of cardan joints is investigated. 2 electric grounded motors are used to drive rabbits passively in terms of predefined trajectories repeatedly with an additional periodical impact loading on the femoral head during motions. It is ideal to simulate usual forceful and repeated actions in sports, such as gymnastics and acrobatics, and initiated a new way to establish animal femoral head AVN models by means of kinematically simulating the real motion procedure rather than traditional medical or physiological operation method. With sets of experiments, the characterization of the exoskeleton system is discussed.