2148
Efficient Tree-Based Revocation in Groups of Low-State Devices
We study the problem of broadcasting confidential information to a collection of n devices while providing the ability to revoke an arbitrary subset of those devices (and tolerating collusion among the revoked devices). In this paper, we restrict our attention to low-memory devices, that is, devices that can store at most O(log n) keys. We consider solutions for both zero-state and low-state cases, where such devices are organized in a tree structure T. We allow the group controller to encrypt broadcasts to any subtree of T, even if the tree is based on an multi-way organizational chart or a severely unbalanced multicast tree.
5712
The rainbow skip graph: a fault-tolerant constant-degree distributed data structure
We present a distributed data structure, which we call the rainbow skip graph. To our knowledge, this is the first peer-to-peer data structure that simultaneously achieves high fault-tolerance, constant-sized nodes, and fast update and query times for ordered data. It is a non-trivial adaptation of the SkipNet/skip-graph structures of Harvey et al. and Aspnes and Shah, so as to provide fault-tolerance as these structures do, but to do so using constant-sized nodes, as in the family tree structure of Zatloukal and Harvey. It supports successor queries on a set of n items using O(log n) messages with high probability, an improvement over the expected O(log n) messages of the family tree. Our structure achieves these results by using the following new constructs:• Rainbow connections: parallel sets of pointers between related components of nodes, so as to achieve good connectivity between "adjacent" components, using constant-sized nodes.• Hydra components: highly-connected, highly fault-tolerant components of constant-sized nodes, which will contain relatively large connected subcomponents even under the failure of a constant fraction of the nodes in the component.We further augment the hydra components in the rainbow skip graph by using erasure-resilient codes to ensure that any large subcomponent of nodes in a hydra component is sufficient to reconstruct all the data stored in that component. By carefully maintaining the size of related components and hydra components to be O(log n), we are able to achieve fast times for updates and queries in the rainbow skip graph. In addition, we show how to make the communication complexity for updates and queries be worst case, at the expense of more conceptual complexity and a slight degradation in the node congestion of the data structure.
878546
The skip quadtree: a simple dynamic data structure for multidimensional data
We present a new multi-dimensional data structure, which we call the skip quadtree (for point data in R2) or the skip octree (for point data in Rd, with constant d > 2). Our data structure combines the best features of two well-known data structures, in that it has the well-defined "box"-shaped regions of region quadtrees and the logarithmic-height search and update hierarchical structure of skip lists. Indeed, the bottom level of our structure is exactly a region quadtree (or octree for higher dimensional data). We describe efficient algorithms for inserting and deleting points in a skip quadtree, as well as fast methods for performing point location, approximate range, and approximate nearest neighbor queries.
14413567
C-Planarity of Extrovert Clustered Graphs
A clustered graph has its vertices grouped into clusters in a hierarchical way via subset inclusion, thereby imposing a tree structure on the clustering relationship. The c-planarity problem is to determine if such a graph can be drawn in a planar way, with clusters drawn as nested regions and with each edge (drawn as a curve between vertex points) crossing the boundary of each region at most once. Unfortunately, as with the graph isomorphism problem, it is open as to whether the c-planarity problem is NP-complete or in P. In this paper, we show how to solve the c-planarity problem in polynomial time for a new class of clustered graphs, which we call extrovert clustered graphs. This class is quite natural (we argue that it captures many clustering relationships that are likely to arise in practice) and includes the clustered graphs tested in previous work by Dahlhaus, as well as Feng, Eades, and Cohen. Interestingly, this class of graphs does not include, nor is it included by, a class studied recently by Gutwenger et al.; therefore, this paper offers an alternative advancement in our understanding of the efficient drawability of clustered graphs in a planar way. Our testing algorithm runs in O(n3) time and implies an embedding algorithm with the same time complexity.
11833312
Text classification improved through multigram models
Classification algorithms and document representation approaches are two key elements for a successful document classification system. In the past, much work has been conducted to find better ways to represent documents. However, most of the attempts rely on certain extra resources such as WordNet, or they face the problem of extremely high dimension. In this paper, we propose a new document representation approach based on n-multigram language models. This approach can automatically discover the hidden semantic sequences in the documents under each category. Based on n-multigram language models and n-gram language models, we put forward two text classification algorithms. The experiments on RCV1 show that our proposed algorithm based on n-multigram models alone can achieve the similar or even better classification performance compared with the classifier based on n-gram models but the model size of our algorithm is much smaller than that of the latter. Another proposed algorithm based on the combination of n-multigram models and n-gram models improves the micro-F1 and macro-F1 values from 89.5% to 92.6% and 87.2% to 91.1% respectively. All these observations support the validity of our proposed document representation approach.
11369262
GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification
Most of current researches on Web page classification focus on leveraging heterogeneous features such as plain text, hyperlinks and anchor texts in an effective and efficient way. Composite kernel method is one topic of interest among them. It first selects a bunch of initial kernels, each of which is determined separately by a certain type of features. Then a classifier is trained based on a linear combination of these kernels. In this paper, we propose an effective way to optimize the linear combination of kernels. We proved that this problem is equivalent to solving a generalized eigenvalue problem. And the weight vector of the kernels is the eigenvector associated with the largest eigen-value. A support vector machine (SVM) classifier is then trained based on this optimized combination of kernels. Our experiment on the WebKB dataset has shown the effectiveness of our proposed method.
1828280
Latent semantic analysis for multiple-type interrelated data objects
Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.
17319020
Synchronization protocols in distributed real-time systems
In many distributed real-time systems, the workload can be modeled as a set of periodic tasks, each of which consists of a chain of subtasks executing on different processors. Synchronization protocols are used to govern the release of subtasks so that the precedence constraints among subtasks are satisfied and the schedulability of the resultant system is analyzable. Tasks have different worst-case and average end-to-end response times when different protocols are used. In this paper, we consider distributed real-time systems with independent, periodic tasks and fixed-priority scheduling algorithms. We propose three synchronization protocols and conduct simulation to compare their performance with respect to the two timing aspects.
17665537
Synthesis of Distributed Processes from Scenario-Based Specifications
Given a set of sequence diagrams, the problem of synthesis is of deciding whether there exists a satisfying object system and if so, synthesize one automatically. It is crucial in the development of complex systems, since sequence diagrams serve as the manifestation of use cases and if synthesizable they could lead directly to implementation. It is even more interesting (and harder) if the synthesized object system is distributed. In this paper, we propose a systematic way of synthesizing distributed processes from Live Sequence Charts. The basic idea is to first construct a CSP specification from the LSC specification, and then use CSP algebraic laws to group the behaviors of each object effectively. The key point is that the behaviors of each object can be decided locally without constructing the global state machine.