10455339
Pollution in P2P file sharing systems
One way to combat P2P file sharing of copyrighted content is to deposit into the file sharing systems large volumes of polluted files. Without taking sides in the file sharing debate, in this paper we undertake a measurement study of the nature and magnitude of pollution in the FastTrack P2P network, currently the most popular P2P file sharing system. We develop a crawling platform which crawls the majority of the FastTrack Network's 20,000+ supernodes in less than 60 minutes. From the raw data gathered by the crawler for popular audio content, we obtain statistics on the number of unique versions and copies available in a 24-hour period. We develop an automated procedure to detect whether a given version is polluted or not, and we show that the probabilities of false positives and negatives of the detection procedure are very small. We use the data from the crawler and our pollution detection algorithm to determine the fraction of versions and fraction of copies that are polluted for several recent and old songs. We observe that pollution is pervasive for recent popular songs. We also identify and describe a number of anti-pollution mechanisms.
8733679
Stochastic Fluid Theory for P2P Streaming Systems
We develop a simple stochastic fluid model that seeks to expose the fundamental characteristics and limitations of P2P streaming systems. This model accounts for many of the essential features of a P2P streaming system, including the peers' realtime demand for content, peer churn (peers joining and leaving), peers with heterogeneous upload capacity, limited infrastructure capacity, and peer buffering and playback delay. The model is tractable, providing closed-form expressions which can be used to shed insight on the fundamental behavior of P2P streaming systems. The model shows that performance is largely determined by a critical value. When the system is of moderate-to-large size, if a certain ratio of traffic loads exceeds the critical value, the system performs well; otherwise, the system performs poorly. Furthermore, large systems have better performance than small systems since they are more resilient to bandwidth fluctuations caused by peer churn. Finally, buffering can dramatically improve performance in the critical region, for both small and large systems. In particular, buffering can bring more improvement than can additional infrastructure bandwidth.
27240142
Two optimal peer selection problems
Many peer computers today participate in peer-to-peer file sharing applications in which the computers contribute storage and bandwidth resources. Of course, applications can only harness the resource pool if peers make available their surplus resources to them. It is widely documented, however, that the P2P systems are havens for "free riders": a significant fraction of users do not contribute any resources, and a minute fraction of users contribute the majority of the resources. Clearly, to improve the performance of existing P2P file sharing systems, and to enable new classes of P2P applications, a compelling incentive system needs to be put in place.
14817913
The FastTrack overlay: A measurement study
Both in terms of number of participating users and in traffic volume, FastTrack is one of the most important applications in the Internet today. Nevertheless, because FastTrack is proprietary and uses encryption, little is understood about FastTrack's overlay structure and dynamics, its messaging protocol, and its index management. We have built two measurement apparatus--the FastTrack Sniffing Platform and the FastTrack Probing Tool--to unravel many of the mysteries behind FastTrack. We deploy the apparatus to study FastTrack's overlay structure and dynamics, its neighbor selection, its use of dynamic port numbers to circumvent firewalls, and its index management. Although this study does not fully solve the FastTrack puzzle, it nevertheless leads to a coherent description of FastTrack and its overlay. Furthermore, we leverage the measurement results to set forth a number of key principles for the design of a successful unstructured P2P overlay. The measurement results and resulting design principles in this paper should be useful for future architects of P2P overlay networks as well as for engineers managing ISPs.
14410544
Fluid modeling of pollution proliferation in P2P networks
P2P systems are highly vulnerable to pollution attacks in which attackers inject multiple versions of corrupted content into the system, which is then further proliferated by unsuspecting users. However, to our knowledge, there are no closed-form solutions that describe this phenomenon, nor are there models that describe how the injection of multiple versions of corrupted content impacts a clients' ability to receive a valid copy. In this paper we develop a suite of fluid models that model pollution proliferation in P2P systems. These fluid models lead to systems of non-linear differential equations. We obtain closed-form solutions for the differential equations; for the remaining models, we efficiently solve the differential equations numerically. The models capture a variety of user behaviors, including propensity for popular versions, abandonment after repeated failure to obtain a good version, freeloading, and local version blacklisting. Our analysis reveals intelligent strategies for attackers as well as strategies for clients seeking to recover non-polluted content within large-scale P2P networks.
14392689
Composition trust bindings in pervasive computing service composition
In pervasive computing, devices or peers may implement or compose services using services from other devices or peers, and may use components from various sources. A composition trust binding is a prescriptive set of rules which defines the combination of allowable components for a particular service or application. Composition trust bindings can be used to protect both the service invocation path as well as the content handling path. The subsidiary relationships addressed by a composition trust binding are typically transparent today, but represent potential security exposure in pervasive computing systems because the subsidiary services or components may have security vulnerabilities. We define the composition trust binding and illustrate its use in the context of rights management and distributed search in personal content publishing. We compare this approach to existing authentication and authorization methods in service composition
16105632
3D Building Detection and Modeling from Aerial LIDAR Data
This paper presents a method to detect and construct a 3D geometric model of an urban area with complex buildings using aerial LIDAR (Light Detection and Ranging) data. The LIDAR data collected from a nadir direction is a point cloud containing surface samples of not only the building roofs and terrain but also undesirable clutter from trees, cars, etc. The main contribution of this work is the automatic recognition and estimation of simple parametric shapes that can be combined to model very complex buildings from aerial LIDAR data. The main components of the detection and modeling algorithms are (i) Segmentation of roof and terrain points. (ii) Roof topology Inference. We introduce the concept of a roof-topology graph to represent the relationships between the various planar patches of a complex roof structure. (iii) Parametric roof composition. Simple parametric roof shapes that can be combined to create a complex roof structure of a building are recognized by searching for sub-graphs in its roof-topology graph. (iv) Terrain Modeling. The terrain is identified and modeled as a triangulated mesh. Finally, we provide experimental results that demonstrate the validity of our approach for rapid and automatic building detection and geometric modeling with real LIDAR data. We are able to model cities and other urban areas at the rate of about 10 minutes per sq. mile on a low-end PC.
24308712
Learning-based building outline detection from multiple aerial images
This paper presents a method for detecting building outlines using multiple aerial images. Since data-driven techniques may not be able to account for variability of building geometry and appearances, a key insight explored in this paper is a combination of model-based data driven front end with data driven learning in the back end for increased detection accuracy. The three main components of the detection algorithm are: (i) initialization. Image intensity and depth information are integrally used to efficiently detect buildings, and a robust rectilinear path finding algorithm is adopted to obtain good initial outlines. The initialization process involves the following steps: detecting location of buildings, determining the dominant orientations and knot points in the building outline and using these to fit the initial outline; (ii) learning. A compact set of building features are defined and learned from the well-delineated buildings, and a tree-based classifier is applied to the whole region to detect any missing buildings and obtain their rough outlines; and (iii) verification and refinement. Learned features are used to remove falsely detected buildings, and all outlines are refined by the deformation of rectilinear templates. The experiments, with improved detection rate and precise outlines, demonstrate the applicability of our algorithm.
2765279
PEET: Prototype Embedding and Embedding Transition for Matching Vehicles over Disparate Viewpoints
This paper presents a novel framework, prototype embedding and embedding transition (PEET), for matching objects, especially vehicles, that undergo drastic pose, appearance, and even modality changes. The problem of matching objects seen under drastic variations is reduced to matching embeddings of object appearances instead of matching the object images directly. An object appearance is first embedded in the space of a representative set of model prototypes (prototype embedding (PE)). Objects captured at disparate temporal and spatial sites are embedded in the space of prototypes that are rendered with the pose of the cameras at the respective sites. Low dimensional embedding vectors are subsequently matched. A significant feature of our approach is that no mapping function is needed to compute the distance between embedding vectors extracted from objects viewed from disparate pose and appearance changes, instead, an embedding transition (ET) scheme is utilized to implicitly realize the complex and non-linear mapping with high accuracy. The heterogeneous nature of matching between high-resolution and low-resolution image objects in PEET is discussed, and an unsupervised learning scheme based on the exploitation of the heterogeneous nature is developed to improve the overall matching performance of mixed resolution objects. The proposed approach has been applied to vehicular object classification and query application, and the extensive experimental results demonstrate the efficacy and versatility of the PEET framework.
2171095
Heterogeneous chip multiprocessors
Heterogeneous (or asymmetric) chip multiprocessors present unique opportunities for improving system throughput, reducing processor power, and mitigating Amdahl's law. On-chip heterogeneity allow the processor to better match execution resources to each application's needs and to address a much wider spectrum of system loads - from low to high thread parallelism - with high efficiency.
12569145
Building segmentation for densely built urban regions using aerial LIDAR data
We present a novel building segmentation system for densely built areas, containing thousands of buildings per square kilometer. We employ solely sparse LIDAR (Light/Laser Detection Ranging) 3D data, captured from an aerial platform, with resolution less than one point per square meter. The goal of our work is to create segmented and delineated buildings as well as structures on top of buildings without requiring scanning for the sides of buildings. Building segmentation is a critical component in many applications such as 3D visualization, robot navigation and cartography. LIDAR has emerged in recent years as a more robust alternative to 2D imagery because it acquires 3D structure directly, without the shortcomings of stereo in un- textured regions and at depth discontinuities. Our main technical contributions in this paper are: (i) a ground segmentation algorithm which can handle both rural regions, and heavily urbanized areas, where the ground is 20% or less of the data, (ii) a building segmentation technique, which is robust to buildings in close proximity to each other, sparse measurements and nearby structured vegetation clutter, and (Hi) an algorithm for estimating the orientation of a boundary contour of a building, based on minimizing the number of vertices in a rectilinear approximation to the building outline, which can cope with significant quantization noise in the outline measurements. We have applied the proposed building segmentation system to several urban regions with areas of hundreds of square kilometers each, obtaining average segmentation speeds of less than three minutes per km2 on a standard Pentium processor. Extensive qualitative results obtained by overlaying the 3D segmented regions onto 2D imagery indicate accurate performance of our system.