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.
15099164
A scalable supervised algorithm for dimensionality reduction on streaming data
Algorithms on streaming data have attracted increasing attention in the past decade. Among them, dimensionality reduction algorithms are greatly interesting due to the desirability of real tasks. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most widely used dimensionality reduction approaches. However, PCA is not optimal for general classification problems because it is unsupervised and ignores valuable label information for classification. On the other hand, the performance of LDA is degraded when encountering limited available low-dimensional spaces and singularity problem. Recently, Maximum Margin Criterion (MMC) was proposed to overcome the shortcomings of PCA and LDA. Nevertheless, the original MMC algorithm could not satisfy the streaming data model to handle large-scale high-dimensional data set. Thus an effective, efficient and scalable approach is needed. In this paper, we propose a supervised incremental dimensionality reduction algorithm and its extension to infer adaptive low-dimensional spaces by optimizing the maximum margin criterion. Experimental results on a synthetic dataset and real datasets demonstrate the superior performance of our proposed algorithm on streaming data.
32457716
Downward Refinement and the Efficiency of Hierarchical Problem Solving
Abstract Analysis and experiments have shown that hierarchical problem solving is most effective when the hierarchy satisfies the downward refinement property (DRP), whereby every abstract solution can be refined to a concrete-level solution without backtracking across abstraction levels. However, the DRP is a strong requirement that is not often met in practice. In this paper we examine the case when the DRP fails, and provide an analytical model of search complexity parameterized by the probability of an abstract solution being refinable. Our model provides a more accurate picture of the effectiveness of hierarchical problem solving. We then formalize the DRP in Abstrips -style hierarchies, providing a syntactic test that can be applied to determine if a hierarchy satisfies the DRP. Finally, we describe an algorithm called Highpoint that we have developed. This algorithm builds on the Alpine algorithm of Knoblock in that it automatically generates abstraction hierarchies. However, it uses the theoretical tools we have developed to generate hierarchies superior to those generated by Alpine . This superiority is demonstrated empirically. 2
18512433
An Algebraic Approach to Conflict Resolution in Planning
This paper presents an algebra for conflict resolution in nonlinear planning. A set of conflicts in a plan is considered as a constraint network. Each node in the network represents a conflict, and is associated with a set of alternative ways for resolving it. Thus, resolving conflicts in a plan corresponds to selecting a set of consistent resolution methods so that, after they are applied to the plan, every conflict can be removed. The paper discusses the representional issues related to the conflict resolution, presents an algebra for resolving conflicts, and illustrates that some modified algorithms for preprocessing networks of constraints can greatly enhance the efficiency of conflict resolution.
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.
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).
645393
Performance of multiprocessor interconnection networks
A tutorial is provided on the performance evaluation of multiprocessor interconnection networks, to guide system designers in their design process. A classification of parallel/distributed systems is followed by a classification of multiprocessor interconnection networks. Basic terminology for performance evaluation is presented. The performance of crossbar interconnection networks, multistage interconnection networks, and multiple-bus systems is then addressed, and a comparison is made along them.<<ETX>>
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.
26868159
A compression-boosting transform for 2D data
In this paper, we present an invertible transform for 2D data which has the objective of reordering the matrix to improve its (lossless) compression at later stages. Given a binary matrix, the transform involves first searching for the largest uniform submatrix, that is, a submatrix solely composed by the same symbol (either 0 or 1) induced by a subset of rows and columns (which are not necessarily contiguous). Then, the rows and the columns are reordered such that the uniform submatrix is moved to the left-upper corner of the matrix. The transform is recursively applied on the rest of the matrix. The recursion is stopped when the partition produces a matrix which is smaller than a predetermined threshold. The inverse transform (decompression) is fast and can be implemented in linear time in the size of the matrix. The effects of the transform on the compressibility of 2D data is studied empirically by comparing the performance of gzip and bzip2 before and after the application of the transform on several inputs. The preliminary results show that the transform boosts compression.
38252541
A Memory Interference Model for Regularly Patterned Multiple Stream Vector Accesses
Most existing analytical models for memory interference generally assume random bank selection for each memory access. In vector computers, however, memory accesses are typically regularly patterned with a number of data items being accessed concurrently from different banks. Very little is known about the queueing behavior of memory interferences in multiple stream vector accesses. This paper presents an analytical model for memory interferences due to vector accesses in multiple vector processor systems. The model captures the effects of both bank conflicts among elements within one vector access stream and conflicts among multiple vector access streams on system performance. The model is based on a closed queueing network assuming an ideal interconnection network. An approximation technique is proposed to solve the memory queueing system that serves customers in a complicated way (non-FIFO). We also carry out extensive simulation experiments to study memory interference and validate our analytical model. Simulation results and analytical results are in a very good agreement, indicating that the model is very accurate. We further validate our analysis by comparing the numerical results obtained from our analytical model with those measurement results that were published by other researchers. Based on our analytical model and simulations, we carry out performance evaluation of the multiple vector processor systems. Our numerical results show that memory access conflicts pose a severe limitation on the number of useful processors in the system, implying that memory system design is essential to high-performance computing. >
42167729
Computation of chain queries in distributed database systems