9354212
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-TD() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches signiicantly out-perform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.
28591822
Multicast Routing with Minimum Energy Cost and Minimum Delay in Wireless Sensor Networks
In this paper, we consider the multicast routing with minimum energy cost and minimum delay (MEMD) in wireless sensor networks under the access control scheme of Spatial Time Division Multiple Access (STDMA). We formulate and explore both MEMD multicast and ME multicast, and show that the latter is just the Maximum Leaf Spanning Tree (MLST) problem, which is NP-complete. A 2-approximation algorithm is proposed for the MLST problem through improving a known one. Based on this algorithm, an approximation algorithm is obtained for the MEMD multicast problem. To further improve the delay result, we provide another approximation algorithm for our main problem using a different approach. These algorithms are all near linear in the size of the network graph, and also are shown to have good performance by simulation results.
9581791
A genetic clustering method for intrusion detection
Traditional intrusion detection methods lack extensibility in face of changing network configurations as well as adaptability in face of unknown attack types. Meanwhile, current machine-learning algorithms need labeled data for training first, so they are computational expensive and sometimes misled by artificial data. In this paper, a new detection algorithm, the Intrusion Detection Based on Genetic Clustering (IDBGC) algorithm, is proposed. It can automatically establish clusters and detect intruders by labeling normal and abnormal groups. Computer simulations show that this algorithm is effective for intrusion detection.
14318300
Features for neural net based region identification of newspaper documents
Several features for neural network based document region identification are tested. Specifically, this paper examines features for non-text region identification. The neural network based region identification algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, graphic, photo, and other region types, and then uses this classification to guide the processing and analysis of the image. The input data are unusually challenging: low quality images of newspaper documents obtained from microfilmed archives. The results compare favorably with other results reported in the literature.
14855965
Detecting VLIW Hard Errors Cost-Effectively through a Software-Based Approach
Research indicates that as technology scales, hard errors such as wear-out errors are increasingly becoming a critical challenge for microprocessor design. While hard errors in memory structures can be efficiently detected by error correction code, detecting hard errors for functional units cost-effectively is a challenging problem. In this paper, we propose to exploit the idle cycles of the under-utilized VLIW functional units to run test instructions for detecting wear-out errors without increasing the hardware cost or significantly impacting performance. We also explore the design space of this software-based approach to balance the error detection latency and the performance for VLIW architectures. Our experimental results indicate that such a software-based approach can effectively detect hard errors with minimum impact on performance for VLIW processors, which is particularly useful for reliable embedded applications with cost constraints.
34066845
Detection of moving cast shadows using image orthogonal transform
In many image and computer vision applications, shadows interfere with fundamental tasks such as moving objects segmentation and tracking. In this paper, a novel method is proposed to detect the moving cast shadows in the scene. The normalized coefficients of orthogonal transform of image block are proved to be illumination invariant and are used to classify moving shadows and foreground objects. Five kinds of orthogonal transform: DCT, DFT, Haar transform, SVD and Hadamard transform, are utilized in our work to detect moving cast shadows. Experimental results show that the proposed method succeeds in detecting moving cast shadows within indoor and outdoor environments
16936043
Complete Anytime Beam Search
Beam search executes a state-space search, but may abandon nonpromising search avenues in order to reduce complexity. Although it has existed for more than two decades and has been applied to many real-world problems, beam search still suffers from the drawback of possible termination with no solution or a solution of unsatisfactory quality. In this paper, we first propose a domain-independent heuristic for node pruning, and a method to reduce the possibility that beam search will fail. We then develop a complete beam search algorithm. The new algorithm can not only find an optimal solution, but can also reach better solutions sooner than its underlying search method. We apply complete beam search to the maximum boolean satisfiability and the symmetric and asymmetric Traveling Salesman Problems. Our experimental results show that the domain-independent pruning heuristic is effective and the new algorithm significantly improves the performance of its underlying search algorithm.
2426741
An analysis and application of distributed constraint satisfaction and optimization algorithms in sensor networks
This research is motivated by a distributed scheduling problem in distributed sensor networks, in which computational resources are scarce. To cope with limited computational resources and restricted real-time requirement, it is imperative to apply distributed algorithms that have low overhead on resource requirement and high anytime performance. In this paper, We study distributed stochastic algorithm (DSA) and distributed breakout algorithm (DBA), two distributed algorithms developed earlier for distributed constraint satisfaction problems. We experimentally investigate their properties and compare their performance using our distributed scheduling problem as a benchmark. We first formulate the scheduling problem as a distributed multi-coloring problem. We then experimentally show that the solution quality and communication cost of DSA exhibit phase-transition or threshold behavior, in that the performance degenerates abruptly and dramatically when the degree of parallel executions of distributed agents increases beyond some critical value. The results show that when controlled properly, DSA is superior to DBA, having better or competitive solution quality and significantly smaller communication cost than DBA. Therefore, DSA is the algorithm of choice for our distributed scan scheduling problem.
18832812
Depth-First vs. Best-First Search: New Results
Best-first search (BFS) expands the fewest nodes among all admissible algorithms using the same cost function, but typically requires exponential space. Depth-first search needs space only linear in the maximum search depth, but expands more nodes than BFS. Using a random tree, we analytically show that the expected number of nodes expanded by depth-first branch-and-bound (DFBnB) is no more than O(d ċ N), where d is the goal depth and N is the expected number of nodes expanded by BFS. We also show that DFBnB is asymptotically optimal when BFS runs in exponential time. We then consider how to select a linear-space search algorithm, from among DFBnB, iterative-deepening (ID) and recursive best first search (RBFS). Our experimental results indicate that DFBnB is preferable on problems that can be represented by bounded-depth trees and require exponential computation; and RBFS should be applied to problems that cannot be represented by bounded-depth trees, or problems that can be solved in polynomial time.
43211087
Simplify Decision Function of Reduced Support Vector Machines
Reduced Support Vector Machines (RSVM) was proposed as the alternate of standard support vector machines (SVM) in order to resolve the difficulty in the learning of nonlinear SVM for large data set problems. RSVM preselects a subset as support vectors and solves a smaller optimization problem, and it performs well with remarkable efficiency on training of SVM for large problem. All the training points of the subset will be support vectors, and more training points are selected into this subset results in high possibility to obtain RSVM with better generalization ability. So we first obtain the RSVM with more support vectors, and selects out training examples near classification hyper plane. Then only these training examples are used as training set to obtain a standard SVM with less support vector than that of RSVM. Computational results show that standard SVMs on the basis of RSVM have much less support vectors and perform equal generalization ability to that of RSVM.
45074176
Knowledge Adoption in Online Communities of Practice
Although researchers and practitioners have recognized that online Communities of Practice (COPs) can be valuable tools for knowledge management, there is little quantitative research about this relatively new phenomenon. This dissertation helps fill the gap by investigating how individual members of text-based, asynchronous online COPs adopt knowledge contributed by others for their own problem solving. 
For this purpose, this dissertation methodologically utilizes dual-process theories of information processing. It first verifies the appropriateness of the theories by replicating previous research in the new domain. Then, the behaviors and effects of factors that are not previously researched in this way, yet especially pertain to online COPs, are investigated. It is hypothesized that genre conformity and information consistency operate as two heuristic cues in the knowledge adoption in online COPs. It is also hypothesized that focused search and the presence of disconfirming information increase elaboration likelihood during dual-process knowledge adoption. These hypotheses were tested and partially supported with survey data collected from members of two online COPs with different characteristics. 
This dissertation contributes to both IS research and the dual-process theories. Contrary to the cue-filtering paradigm of previous media research, it expands the computer-mediated communication (CMC) literature by identifying additional cues that are utilized for CMC-based knowledge adoption. It invokes the notion that the CMC context plays a dynamic role in ongoing knowledge adoption processes, both through elaboration likelihood and through interactions between the dual processes. Moreover, it advances the situated view of knowledge adoption by demonstrating that how members search for information and how current information compares to prior knowledge matter in online COPs. Focusing on the pull technology of online COPS, it offers another theoretical link between CMC technologies and knowledge management. 
Practically, this dissertation provides us with new insights into the utilization of online COPs for improving organizational knowledge management. It also suggests new directions for designing software systems to better support knowledge sharing in online COPs.