9667404
Power-efficient access-point selection for indoor location estimation
An important goal of indoor location estimation systems is to increase the estimation accuracy while reducing the power consumption. In this paper, we present a novel algorithm known as CaDet for power-efficient location estimation by intelligently selecting the number of access points (APs) used for location estimation. We show that by employing machine learning techniques, CaDet is able to use a small subset of the APs in the environment to detect a client's location with high accuracy. CaDet uses a combination of information theory, clustering analysis, and a decision tree algorithm. By collecting data and testing our algorithms in a realistic WLAN environment in the computer science department area of the Hong Kong University of Science and Technology, we show that CaDet (clustering and decision tree-based method) can be much higher in accuracy as compared to other methods. We also show through experiments that, by intelligently selecting APs, we are able to save the power on the client device while achieving the same level of accuracy.
17216165
Cut-and-Pick Transactions for Proxy Log Mining
Web logs collected by proxy servers, referred to as proxy logs or proxy traces, contain information about Web document accesses by many users against many Web sites. This "many-to-many" characteristic poses a challenge to Web log mining techniques due to the difficulty in identifying individual access transactions. This is because in a proxy log, user transactions are not clearly bounded and are sometimes interleaved with each other as well as with noise. Most previous work has used simplistic measures such as a fixed time interval as a determination method for the transaction boundaries, and has not addressed the problem of interleaving and noisy transactions. In this paper, we show that this simplistic view can lead to poor performance in building models to predict future access patterns. We present a more advanced cut-and-pick method for determining the access transactions from proxy logs, by deciding on more reasonable transaction boundaries and by removing noisy accesses. Our method takes advantage of the user behavior that in most transactions, the same user typically visits multiple, related Web sites that form clusters. These clusters can be discovered by our algorithm based on the connectivity among Web sites. By using real-world proxy logs, we experimentally show that this cut-and-pick method can produce more accurate transactions that result in Web-access prediction models with higher accuracy.
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
13595177
Multiple-Goal Recognition from Low-Level Signals
Researchers and practitioners from both the artificial intelligence and pervasive computing communities have been paying increasing attention to the task of inferring users' high-level goals from low-level sensor readings. A common assumption made by most approaches is that a user either has a single goal in mind, or achieves several goals sequentially. However, in real-world environments, a user often has multiple goals that are concurrently carried out, and a single action can serve as a common step towards multiple goals. In this paper, we formulate the multiple-goal recognition problem and exemplify it in an indoor environment where an RF-based wireless network is available. We propose a goal-recognition algorithm based on a dynamic model set and show how goal models evolve over time based on pre-defined states. Experiments with real data demonstrate that our method can accurately and efficiently recognize multiple interleaving goals in a user's trace.
9645876
Handling feature interactions in process-planning
Abstract The development of a successful approach to the integration of computer-aided design and computer-aided process-planning will require solutions to several fundamental problems in planning and geometric reasoning. In this article, we consider two such problems, both of which arise from interactions among machinable features. The first problem is that interactions can lead to several alternative interpretations of what the machinable features are. We address this problem by developing an algebra of features. The second problem is that interactions among features can cause problems in optimizing the process plans. For this problem, we have developed a branch-and-bound optimization algorithm with good heuristics to guide the search. This article presents the theory and mathematical framework for both problems and also the details of the implementation and experimental results.
11767978
Real-Time Scheduling for Multi-agent Call Center Automation
In a call center, service agents with different capabilities are available for solving incoming customer problems at any time. To supply quick response and better problem solution to customers, it is necessary to schedule customer problems to appropriate service agents efficiently. We developed SANet, a service agent network for call center, which integrates multiple service agents including both software agents and human agents, and employs a broker to schedule customer problems to service agents for better solutions according to their changing capabilities and availability. This paper describes the real-time scheduling method in SANet as well as its architecture. There are two phases in our scheduling method. One is problem-type learning. The broker is trained to learn the problem types and hence can decide the type of incoming problems automatically. The other is the scheduling algorithm based on problem types, capabilities and availability of service agents. We highlight an application in which we apply SANet to a call center problem for a cable-TV company. Finally, we support our claims via experimental results and discuss related works.
36641724
A new and efficient FFT algorithm for distributed memory systems
This paper presents a new and optimal parallel implementation of multidimensional fast Fourier transform algorithm on distributed memory multiprocessors. Its optimality is obtained by minimizing the number of message passings necessary, at the cost of increase in message length. This distinctive feature of the new algorithm effectively utilizes the important architectural property of most of today's distributed memory multiprocessors-wormhole routing for interprocessor communications. By using the algebra of stride permutations and tenser products as a mathematical tool, we are able to derive and formulate an efficient data partition and communication scheme that reduces communication cost from O(N/sup 2/) required for the best known FFT to O(N) on an N/sup 2/-processor machine. Our data partition scheme is natural and efficient for solving discretized boundary value problems such as partial differential equations and finite element analysis. To evaluate the actual performance of our new algorithm in comparison with other existing parallel FFT algorithms, we have carried out implementation experiments on the Intel's Touchstone Delta machine.
13076928
Coverage Algorithm and Protocol in Heterogeneous Sensor Networks
One fundamental issue in wireless sensor networks is the coverage problem. In heterogeneous sensor networks composed of different types of sensors, the difference of the sensing radius of nodes augments the computation difficulty of coverage degree. In this paper, we analyze the features of heterogeneous sensor networks and discuss the approaches to guarantee and calculate the coverage degree of the region deployed by heterogeneous sensor networks. Based on our analysis, a Distributed Coverage Optimization Algorithm by which each node in the network can determine whether it should be turn active/inactive is proposed. Simulation shows that our algorithm can make the extraneous nodes inactive and minimize the number of nodes need to remain active.
1859019
A parallel algorithm for clustering protein-protein interaction networks
The increasing availability of interaction graphs requires new resource-efficient tools capable of extracting valuable biological knowledge from these networks. In this paper we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that is capable of running on clusters of computers. Our parallel implementation achieves almost linear speed-up up to 32 processors and allows us to run this computationally intensive algorithm on large protein-protein interaction networks. Preliminary experiments show that the algorithm has very high accuracy in identifying functional related protein modules.
27730185
Parallel All-Row Preconditioned Interval Linear Solver for Nonlinear Equations on Multiprocessors
Interval Newton methods in conjunction with generalized bisection form the basis of algorithms that find all real roots within a specified X ⊂ Rn of a system of nonlinear equations F(X) = 0 with mathematical certainty, even in finite-precision arithmetic. One of the major computational cost in such methods is solving a correspondent linear interval system. This paper proposes parallel implementations of the all-row preconditioned interval linear solver on two multiprocessor architectures: shared bus multiprocessor and hypercube connected multiprocessor. Efficient parallel algorithms that are specifically tailored for these architectures are presented. The algorithms effectively use the hardware features to minimize communication overheads. Performance evaluation is carried out by means of actual measurements of the algorithms running on real parallel computers. Our numerical results show that data partitioning, data allocation and processor scheduling play important roles in the performance of the parallel computation. In both systems, significant speedup can be obtained by properly overlapping computation and communication.
6583750
Building Trust in Online Rating Systems Through Signal Modeling
Online feedback-based rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of regular ratings is relatively small and the unfair ratings contribute to a significant portion of the overall ratings. In this paper, we propose a novel algorithm to detect the unfair ratings that cannot be effectively prevented by existing state-of-the-art techniques. Our algorithm is particularly effective to detect malicious raters that collaboratively manipulate ratings of one or several products. The main idea of our algorithm is to use an autoregressive signal modeling technique combined with trust-enhanced rating aggregation. We are able to detect and filter out unfair ratings very accurately. Extensive experiments through simulations and real-world data have been performed to validate the proposed algorithm. The experimental results show significant improvements on detecting collaborative unfair raters over existing techniques.