Qualitative probabilistic networks with reduced ambiguities

A Qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network that encodes variables and the qualitative influences between them. In order to make QPNs be practical for real-world representation and inference of uncertain knowledge, it is desirable to reduce ambiguities in general QPNs, including unknown qualitative influences and inference conflicts. In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold. In addition, we introduce probabilistic-rough-set-based weights to the qualitative influences. The enhanced network so obtained, called EQPN, is constructed from sample data. Finally, to achieve conflict-free EQPN inferences, we resolve the trade-offs by addressing the symmetry, transitivity and composition properties. Preliminary experiments verify the correctness and feasibility of our methods.     

Information networks: A probabilistic model for hierarchical message transfer

A strictly hierarchical message transfer scheme requires that a message follow a specified referral path unless finally it is either rejected or filled at any one of the information centers of the network. Thus at each node in the network three decisions can be made: satisfy, reject, or refer the message to the succeeding node in the hierarchy. By associating probabilities and costs with each of these decisions, we develop a Markovian model for the total network cost. The mean and variance of total cost are derived. Applicability of the model is discussed by considering the problems related to the estimation of necessary parameters. In particular, a queue-theoretic model is developed for estimating response time for a message at an information center.     

Labeling evaluation in probabilistic networks

In many pattern recognition problems a probabilistic labeling of a network is given, and it is desired to obtain a unique unambiguous labeling for the network. This labeling should be influenced by the given probabilistic labeling, and by the joint distributions of the labels at subsets of nodes of the network. Relaxation algorithms have frequently been used to find such a labeling, but no method has been available to evaluate the results or to compare two different labelings. A measure is proposed here for evaluating labelings based on the given probabilistic labeling and joint distributions. This measure can also be used to define a termination criterion for relaxation by evaluating the labeling at each iteration. In addition, it could be used to evaluate labelings derived by any other process, and to guide heuristic search.     

Shortest paths in stochastic networks with correlated link costs

The objective is to minimize expected travel time from any origin to a specific destination in a congestible network with correlated link costs. Each link is assumed to be in one of two possible conditions. Conditional probability density functions for link travel times are assumed known for each condition. Conditions over the traversed links are taken into account for determining the optimal routing strategy for the remaining trip. This problem is treated as a multistage adaptive feedback control process. Each stage is described by the physical state (the location of the current decision point) and the information state (the service level of the previously traversed links). Proof of existence and uniqueness of the solution to the basic dynamic programming equations and a solution procedure are provided.     

Composition of probabilistic evaluations of preferences: a case of criteria applied to isolated and clustered options

This article applies a transformation into probabilities of preference, to rank papers submitted to the XLI Brazilian Symposium of Operational Research. Some 42 papers were divided into sets according to their main topics of interest and measures for 12 attributes were considered, for each paper separately, and also for sets of papers on the same topic, based on the referees' evaluations. Four alternative forms of composition of the probabilistic preferences are illustrated.     

Node-disjoint paths in incomplete WK-recursive networks

The incomplete WK-recursive networks have been recently proposed to relieve the restriction on the sizes of the WK-recursive networks. In this paper, a maximal set of node-disjoint paths is constructed between arbitrary two nodes of an incomplete WK-recursive network. The effectiveness of the constructed paths is verified by both theoretic analysis and extensive experiments. A tight upper bound on the maximal length is suggested. On the other hand, experimental results show that for arbitrary two nodes, the expected maximal length is not greater than twice their distance and about equal to the diameter. When the two nodes are the farthest pair, the maximal length is not greater than twice the diameter and the expected maximal length is not greater than 1.5 times the diameter.     

Routing permutations on baseline networks with node-disjoint paths

Permutation is a frequently-used communication pattern in parallel and distributed computing systems and telecommunication networks. Node-disjoint routing has important applications in guided wave optical interconnects where the optical "crosstalk" between messages passing the same switch should be avoided. In this paper, we consider routing arbitrary permutations on an optical baseline network (or reverse baseline network) with node-disjoint paths. We first prove the equivalence between the set of admissible permutations (or semipermutations) of a baseline network and that of its reverse network based on a step-by-step permutation routing. We then show that an arbitrary permutation can be realized in a baseline network (or a reverse baseline network) with node-disjoint paths in four passes, which beats the existing results [M. Vaez et al., (2000)], [G. Maier et al., (2001)] that a permutation can be realized in an n /spl times/ n banyan network with node-disjoint paths in O(n/sup 1/2/) passes. This represents the currently best-known result for the number of passes required for routing an arbitrary permutation with node-disjoint paths in unique-path multistage networks. Unlike other unique path MINs (such as omega networks or banyan networks), only baseline networks have been found to possess such four-pass routing property. We present routing algorithms in both self-routing style and central-controlled style. Different from the recent work in [Y. Yang et al., (2003)], which also gave a four-pass node-disjoint routing algorithm for permutations, the new algorithm is efficient in transmission time for messages of any length, while the algorithm in [Y. Yang et al., (2003)] can work efficiently only for long messages. Comparisons with previous results demonstrate that routing in a baseline network proposed in this paper could be a better choice for routing permutations due to its lowest hardware cost and near-optimal transmission time.     

A probabilistic rating inference framework for mining user preferences from reviews

We propose a novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. Pref applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. Pref takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of Pref against several related algorithms, and suggest that Pref can produce reasonably good results using a small training corpus. We also describe a useful application of Pref as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated Pref with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.     

Node-disjoint paths in hierarchical hypercube networks

The hierarchical hypercube network is suitable for massively parallel systems. One of its appealing properties is the low number of connections per processor, which can facilitate the VLSI design and fabrication. Other alluring features include symmetry and logarithmic diameter, which can derive easy and fast algorithms for communication. In this paper, a maximal number of node-disjoint paths are constructed between every two distinct nodes of the hierarchical hypercube network. Their maximal length is not greater than max{2^m+1+2m+1,2^m+1+m+4}, where 2^m+1 is the diameter. The effectiveness of node-disjoint paths is further verified by experiments.     

CROSS: A probabilistic constrained random sensor selection scheme in wireless sensor networks

Due to the application-specific nature of wireless sensor networks, the sensitivity to coverage and data reporting latency varies depending on the type of applications. In light of this, algorithms and protocols should be application-aware to achieve the optimum use of highly limited resources in sensors and hence to increase the overall network performance. This paper proposes a probabilistic constrained random sensor selection (CROSS) scheme for application-aware sensing coverage with a goal to maximize the network lifetime. The CROSS scheme randomly selects in each round (approximately) k data-reporting sensors which are sufficient for a user/application-specified desired sensing coverage (DSC) maintaining a minimum distance between any pair of the selected k sensors. We exploit the Poisson sampling technique to force the minimum distance. Consequently, the CROSS improves the spatial regularity of randomly selected k sensors and hence the fidelity of satisfying the DSC in each round, and the connectivity among the selected sensors increase. To this end, we also introduce an algorithm to compute the desired minimum distance to be forced between any pair of sensors. Finally, we present the probabilistic analytical model to measure the impact of the Poisson sampling technique on selecting k sensors, along with the optimality of the desired minimum distance computed by the proposed algorithm.     

Context-specific sign-propagation in qualitative probabilistic networks

Qualitative probabilistic networks are qualitative abstractions of probabilistic networks, summarising probabilistic influences by qualitative signs. As qualitative networks model influences at the level of variables, knowledge about probabilistic influences that hold only for specific values cannot be expressed. The results computed from a qualitative network, as a consequence, can be weaker than strictly necessary and may in fact be rather uninformative. We extend the basic formalism of qualitative probabilistic networks by providing for the inclusion of context-specific information about influences and show that exploiting this information upon reasoning has the ability to forestall unnecessarily weak results.     

A probabilistic and opportunistic flooding algorithm in wireless sensor networks

In wireless sensor networks, many communication protocols and applications rely on flooding for various networking purposes. Prior efforts focus on how to design efficient flooding algorithms; that is, they seek to achieve full reliability while reducing the number of redundant broadcasting across the network. To achieve efficient flooding, most of the existing protocols try to reduce the number of transmissions, which is decided without considering any online transmission result. In this paper, we propose a probabilistic and opportunistic flooding algorithm that controls rebroadcasts and retransmissions opportunistically. It seeks to achieve a target reliability required by an application. For this purpose, it makes a given node select only the subset of its one-hop neighbors to rebroadcast the same message. It considers node relations such as link error rates among nodes in selecting eligible neighbors to rebroadcast. The sender controls the number of retransmissions opportunistically by tracking the current status of message reception at its neighbors. Simulation is carried out to reveal that our proposed scheme achieves the given target reliability with less overhead than other flooding algorithms in most cases, thus prolonging the network lifetime.     

User-expertise modeling with empirically derived probabilistic implication networks

The application of user-expertise modeling for adaptive interfaces is confronted with a number of difficult challenges, namely, efficiency and reliability, the cost-benefit ratio, and the practical usability of user modeling techniques. We argue that many of these obstacles can be overcome by standard, automatic means of performing knowledge assessment. Within this perspective, we present the basis of a probabilistic user modeling approach, the POKS technique, which could serve as a standard user-expertise modeling tool.The POKS technique is based on the cognitive theory of knowledge structures: a formalism for the representation of the order in which we learn knowledge units (KU). The technique permits the induction of knowledge structures from a small number of empirical data cases. It uses an evidence propagation scheme within these structures to infer an individual's knowledge state from a sample of KU. The empirical induction technique is based, in part, on statistical hypothesis testing over conditional probabilities that are determined by the KUs' learning order.Experiments with this approach show that the technique is successful in partially inferring an individual's knowledge state, either through the monitoring of a user's behavior, or through a selective questioning process. However, the selective process, based on entropy minimization, is shown to be much more effective in reducing the standard error score of knowledge assessment than random sampling.     

Causal probabilistic networks with both discrete and continuousvariables

An extension of the expert system shell known as handling uncertainty by general influence networks (HUGIN) to include continuous variables, in the form of linear additive normally distributed variables, is presented. The theoretical foundation of the method was developed by S.L. Lauritzen, whereas this report primarily focus on implementation aspects. The approach has several advantages over purely discrete systems. It enables a more natural model of of the domain in question, knowledge acquisition is eased, and the complexity of belief revision is most often reduced considerably     

Surveillance with wireless sensor networks in obstruction: Breach paths as watershed contours

For surveillance applications of wireless sensor networks, analysis of sensing coverage and quality of sensing is crucial. For rough terrains where obstacles block the sensing capability, region-based approaches must be employed to determine the sensing quality. In this paper, we present a method to determine the breach paths and the deployment quality defined as the minimum of the maximum detection probabilities on the breach paths in the presence of obstacles. We propose the utilization of watershed segmentation on the iso-sensing map that reveals the equally-sensed regions of the field-of-interest in a surveillance application. Probabilistic sensor models are utilized to produce the iso-sensing map considering the sensing coverage degree and reliability level as the design criteria. The watershed segmentation algorithm is applied on the iso-sensing map to identify the possible breach paths. An algorithm is proposed to convert the watershed segmentation to an auxiliary graph which is then employed to determine the deployment quality measure (DQM). The effects of the sensor count and coverage degree on the DQM are analyzed.     

Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting

Feed-forward neural networks (FFNNs) are among the most important neural networks that can be applied to a wide range of forecasting problems with a high degree of accuracy. Several large-scale forecasting competitions with a large number of commonly used time series forecasting models conclude that combining forecasts from more than one model often leads to improved performance, especially when the models in the ensemble are quite different. In the literature, several hybrid models have been proposed by combining different time series models together. In this paper, in contrast of the traditional hybrid models, a novel hybridization of the feed-forward neural networks (FFNNs) is proposed using the probabilistic neural networks (PNNs) in order to yield more accurate results than traditional feed-forward neural networks. In the proposed model, the estimated values of the FFNN models are modified based on the distinguished trend of their residuals and optimum step length, which are respectively yield from a probabilistic neural network and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than FFNN models. Therefore, it can be applied as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.     

基于SCNF和概率CYK算法的最佳剖析树

对基于纯规则(chart)的自底向上方法进行句法剖析后出现的大量无法消解的歧义现象,通过引入概率型Chomsky范式(SCNF)在一定程度上消除部分句法结构歧义;在利用Inside-Outside算法进行参数自动训练,并通过语法示例验证Inside-Outside算法的收敛性后,最后采用概率CYK算法得到句子的最佳剖析树.