7197213
A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking
The ability to accurately detect the location of a mobile node in a sensor network is important for many artificial intelligence (AI) tasks that range from robotics to context-aware computing. Many previous approaches to the location-estimation problem assume the availability of calibrated data. However, to obtain such data requires great effort. In this paper, we present a manifold regularization approach known as LeMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. We compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use Crossbow MICA2 to setup the network and USB camera array to obtain the ground truth. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared to several previous systems.
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.
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.
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.
13194241
BUCS - a bottom-up cache structure for networked storage servers
This paper introduces a new caching structure to improve server performance by minimizing data traffic over the system bus. The idea is to form a bottom-up caching hierarchy in a networked storage server. The bottom level cache is located on an embedded controller that is a combination of a network interface card (NIC) and a storage host bus adapter (HBA). Storage data coming from or going to a network are cached at this bottom level cache and meta-data related to these data are passed to the host for processing. When cached data exceed the capacity of the bottom level cache, some data are moved to the host RAM that is usually larger than the bottom level cache. This new cache hierarchy is referred to as bottom-up cache structure (BUGS) in contrast to a traditional CPU-centric top-down cache where the top-level cache is the smallest and fastest, and the lower in the hierarchy the larger and slower the cache. Such data caching at the controller level dramatically reduces bus traffic and leads to great performance improvement for networked storages. We have implemented a proof-of-concept prototype using Intel's IQ80310 reference board and Linux network block device. Through performance measurements on the prototype implementation, we observed up to 3 times performance improvement of BUCS over traditional systems in terms of response time and system throughput.
7030953
Energy-aware partitioning for multiprocessor real-time systems
In this paper, we address the problem of partitioning periodic real-time tasks in a multiprocessor platform by considering both feasibility and energy-awareness perspectives: our objective is to compute the feasible partitioning that results in minimum energy consumption on multiple identical processors by using variable voltage earliest-deadline-first scheduling. We show that the problem is NP-hard in the strong sense on m /spl ges/ 2 processors even when feasibility is guaranteed a priori. Then, we develop our framework where load balancing plays a major role in producing energy-efficient partitionings. We evaluate the feasibility and energy-efficiency performances of partitioning heuristics experimentally.
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.
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.
2657730
Approximate Analysis of Single and Multiple Ring Networks
Asynchronous packet-switched interconnection networks with decentralized control are very appropriate for multiprocessing and data-flow architectures. The authors present performance models of single- and multiple-ring networks based on token-ring, slotted-ring, and register-insertion-ring protocols. The multiple ring networks have the advantage of being reliable, expandable, and cost effective. An approximate and uniform analysis, based on the gate M/G/1 queuing model, has been developed to evaluate the performance of both existing single-ring networks and the proposed multiple-ring networks. Approximations are good for low and medium load. The analyses are based on symmetric ring structure with nonexhaustive service policy and infinite queue length at each station. They essentially involve modeling of queues with single- and multiple-walking servers. The results obtained from the analytical models are compared to those obtained from simulation. >