40464778
A model for compressing probabilities in belief networks
Probabilistic reasoning with belief (Bayesian) networks is based on conditional probability matrices. Thus it suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary for the computations is often overwhelming. So, compressing the conditional probability table is one of the most important issues faced by the probabilistic reasoning community. Santos suggested an approach (called linear potential functions) for compressing the information from a combinatorial amount to roughly linear in the number of random variable assignments. However, much of the information in Bayesian networks, in which there are no linear potential functions, would be fitted by polynomial approximating functions rather than by reluctantly linear functions. For this reason, we construct a polynomial method to compress the conditional probability table in this paper. We evaluated the proposed technique, and our experimental results demonstrate that the approach is efficient and promising.
31582417
An Agent-Based Soft Computing Society
Soft computing (SC) techniques such as fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are complementary. Each SC technique has particular computational properties that make them suited for particular problems and not for others. Thus, in solving complex, real-world problems, we need to incorporate some SC techniques into the application systems to increase the systems' "intelligence". In this paper, we first propose an agent-based framework for integrating SC techniques into practical application systems. We then discuss the design and implementation of a platform independent soft computing support environment based on the framework. We call such an environment agent-based soft computing society. Such a society can facilitate the design of truly robust, flexible and adaptive hybrid intelligent systems.
40273501
Pattern Discovery in Probabilistic Databases
Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don't work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.
6431749
Reasoning with Multimedia Information Using Symbolic Projection
In many multimedia application systems, it is not the final goal to retrieve the relevant multimedia information from different multimedia information sources. Rather, post-processing of the retrieved multimedia information is needed. For example, the retrieved information is used as "known facts". The systems will do some reasoning to obtain further conclusions based on these multimedia form "known facts". We call this reasoning with multimedia information. Most current research work in multimedia information processing is focused on multimedia information retrieval, but post-processing the retrieved information is more or less ignored. This paper explores the way to tackle this problem by using symbolic projection. A case study of reasoning with still image information is presented. Some extensions to symbolic projection-introducing auxiliary pictorial objects in symbolic pictures that need to be processed-are discussed. We expect this paper will stimulate further research on this important but ignored topic.
1498226
Approaches to implementing decision aggregation in multi-agent systems
14748826
QoS-Aware Optimization of Composite-Service Fulfillment Policy
In a service-oriented IT infrastructure, functional capabilities of a computing component are externalized via one or more service interfaces. Driven by the demand for business agility and return-on-investment optimization, various dynamic service discovery and composition technologies have been proposed and developed with a common goal of enabling business-aligned fulfillment of customer requests. However, from the viewpoint of capacity planning and IT optimization, much work is still needed in helping an enterprise decide the "optimal" IT resources necessary for deploying the atomic services in support of those composite ones. The service deployment decision must be integrated with the request fulfillment policy so that the differentiated quality-of-service (QoS) requirements of service requests can be met, for instance, with minimum hardware/software cost. In this paper, we propose an approach for QoS-aware optimization of composite-service fulfillment policy. Without loss of generality, we assume that the optimization goal is to minimize the number of machines subject to response time and throughput requirements. After presenting our approach to the optimization problem using the assumption, we show that an NP-hard throughput optimization problem must be attacked. We then illustrate how we attack the problem via an efficient heuristic algorithm. The algorithm decomposes the end-to-end response time requirement for each type of composite service into atomic-service level response time assurance, and co-locate atomic services with similar response time assurance on machines with similar utilization characteristics. The algorithm exemplifies an integrated approach to optimizing service deployment and service composition. We demonstrate that the algorithm achieves a substantially higher throughput than a common baseline algorithm.
11885669
A decision tree-based multimodal data mining framework for soccer goal detection
We propose a new multimedia data mining framework for the extraction of soccer goal events in soccer videos by using combined multimodal analysis and decision tree logic. The extracted events can be used to index the soccer videos. We first adopt an advanced video shot detection method to produce shot boundaries and some important visual features. Then, the visual/audio features are extracted for each shot at different granularities. This rich multimodal feature set is filtered by a pre-filtering step to clean the noise as well as to reduce the irrelevant data. A decision tree model is built upon the cleaned data set and is used to classify the goal shots. Finally, the experimental results demonstrate the effectiveness of our framework for soccer goal extraction.
14018437
Parallel SOR Iterative Algorithms and Performance Evaluation on a Linux Cluster
Abstract : The successive over-relaxation (SOR) iterative method is an important solver for linear systems. In this paper, a parallel algorithm for the red-black SOR method with domain decomposition is investigated. The parallel SOR algorithm is designed by combining the traditional red-black SOR and row block domain decomposition technique, which reduces the communication cost and simplifies the parallel implementation. Two other iterative methods, Jacobi and Gauss-Seidel (G-S), ate also implemented in parallel for comparison. The three parallel iterative algorithms are implemented in C and MPI (Message Passing Interface) for solving the Dirichlet problem on a Linux cluster with eight dual processor 2.6ghz 32 bit Intel Xeons, totaling 16 processors. The performances of the three algorithms are evaluated in terms of speedup and efficiency.
5928457
Permutation graphs and Petersen graph
1297870
An active learning framework for content-based information retrieval
We propose a general active learning framework for content-based information retrieval. We use this framework to guide hidden annotations in order to improve the retrieval performance. For each object in the database, we maintain a list of probabilities, each indicating the probability of this object having one of the attributes. During training, the learning algorithm samples objects in the database and presents them to the annotator to assign attributes. For each sampled object, each probability is set to be one or zero depending on whether or not the corresponding attribute is assigned by the annotator. For objects that have not been annotated, the learning algorithm estimates their probabilities with biased kernel regression. Knowledge gain is then defined to determine, among the objects that have not been annotated, which one the system is the most uncertain. The system then presents it as the next sample to the annotator to which it is assigned attributes. During retrieval, the list of probabilities works as a feature vector for us to calculate the semantic distance between two objects, or between the user query and an object in the database. The overall distance between two objects is determined by a weighted sum of the semantic distance and the low-level feature distance. The algorithm is tested on both synthetic databases and real databases of 3D models. In both cases, the retrieval performance of the system improves rapidly with the number of annotated samples. Furthermore, we show that active learning outperforms learning based on random sampling.