18611349
Online Adaptive Fault-Tolerant Routing in 2D Torus
In this paper, we propose efficient routing algorithms for 2D torus with possible large number of faulty nodes. There is no presumption on the number and the distribution of faulty nodes. The proposed algorithms find a fault-free path between any two nonfaulty nodes with high probability in linear time by using only the local routing information of the network. The results of our empirical analysis through simulations show that the algorithms can find a fault-free path between any two nonfaulty nodes with high probability. For example, in a torus of size up to 128×128, where, the number of faulty nodes up to 15%, the heuristuc-square routing algorithm finds a fault-free path with a probability of 90% or higher. The experimental results are impressive for 2D torus with only four links per node.
5296800
Adaptive box-based efficient fault-tolerant routing in 3D torus
In this paper, we propose efficient fault-tolerant routing algorithms for 3D torus with possible large number of faulty nodes. There is no any presumption on the number and the distribution of faulty nodes. The proposed algorithms find a fault-free path between any two nonfaulty nodes with high probability in linear time by using only the local faulty information of the network. The results of our empirical analysis through simulations show that the algorithms can find a fault-free path between any two nonfaulty nodes with a probability higher than 90% in a 3D torus with the number of faulty nodes up to 30%.
14671442
An Approach to Outsourcing Data Mining Tasks while Protecting Business Intelligence and Customer Privacy
Data mining is playing an important role in decision making. It is beneficial to outsource data mining tasks if an organization does not have required expertise in-house. However, the organization may lose business intelligence and customer privacy during this outsourcing process. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules while protecting their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off storage requirements
5886737
Using KCCA for Japanese–English cross-language information retrieval and document classification
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese–English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.
12780409
Discovering Temporal Patterns in Multiple Granularities
Many events repeat themselves as the time goes by. For example, an institute pays its employees on the first day of every month. However, events may not repeat with a constant span of time. In the payday example here, the span of time between each two consecutive paydays ranges between 28 and 31 days. As a result, regularity, or temporal pattern, has to be captured with a use of granularities (such as day, week, month, and year), oftentimes multiple granularities. This paper defines the above patterns, and proposes a number of pattern discovery algorithms. To focus on the basics, the paper assumes that a list of events with their timestamps is given, and the algorithms try to find patterns for the events. All of the algorithms repeat two possibly interleaving steps, with the first step generating possible (called candidate) patterns, and the second step verifying if candidate patterns satisfy some user-given requirements. The algorithms use pruning techniques to reduce the number of candidate patterns, and adopt a data structure to efficiently implement the second step. Experiments show that the pruning techniques and the data structure are quite effective.
8996068
A mobile sensor network using autonomously controlled animals
Recent research has demonstrated that it is possible to use wireless communication to deliver brain stimulation to guide the movements of rats through a variety of terrains by stimulating multiple brain regions to produce stimulus cues for various commanded movements. The rats can be controlled not only while in direct line of sight of a human controller, but also by teleoperation, using signals transmitted from a wireless video camera mounted on the animal's backpack. Remotely guided rats or other animals are ideal for search and rescue operations because they are highly adept at negotiating difficult 3D terrain, in both light and dark. The animals autonomously choose their own methods for traversing particular obstacles. Primarily for these reasons, they can be more effective than mechanical robots in search and rescue applications. They can also be trained to detect and home in on specific sensory targets, allowing them to be used as biosensors. To meet these challenges, a cluster of interrelated research studies, including network topology design, routing algorithms, media access control techniques, radio resource management, radio propagation modeling, and mobility modeling are comprised. In this paper, the authors have designed and implemented the hardware of backpacks and set up a simple wireless ad hoc network by using static routing.
29006676
A Remote Resource Monitoring Approach in Active Networks
Monitoring the resource status in active network nodes is important both for the network administrator to maintain and deploy active networks and for the normal end-users to acquire better performance. Hence, a method that can monitor the resource status at a remote node is needed. Moreover, due to the complexity of the resource usage in active networks, the corresponding method should be able to monitor the status of various resources at remote node dynamically and in a user-specified way. This paper presents the design and implementation of such a resource monitoring method. The method adopts the active application technique to obtain the desired resource status information at a remote network node and transfer them back to the user in the user-specified way. It also allows the user to control the content, the procedure and the precise of the monitoring.