5672538
When do Numbers Really Matter?
Common wisdom has it that small distinctions in the probabilities quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network probabilities can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytical results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.
257219
On the Revision of Probabilistic Beliefs using Uncertain Evidence
We revisit the problem of revising probabilistic beliefs using uncertain evidence, and report results on several major issues relating to this problem: how should one specify uncertain evidence? How should one revise a probability distribution? How should one interpret informal evidential statements? Should, and do, iterated belief revisions commute? And what guarantees can be offered on the amount of belief change induced by a particular revision? Our discussion is focused on two main methods for probabilistic revision: Jeffrey's rule of probability kinematics and Pearl's method of virtual evidence, where we analyze and unify these methods from the perspective of the questions posed above.
2499858
Location updates and probabilistic tracking algorithms for mobile cellular networks
In this paper, we develop a novel mathematical model to analyze different location update protocols for mobile cellular network. Our model can capture many important features of user movements, such as geographically dependent movement, directional movement and non-exponential distribution of cell dwell times. We propose to use the transient method to predict the location of a mobile user. This method is efficient and, more importantly, able to address the temporal change in the current location of a mobile user. Therefore, the accuracy of the prediction of the location of a user can be enhanced. We also propose a new location update protocol which can attain a lower trading cost and also adapt to different mobility patterns. Transient performance analysis is also carried out and the results are compared with other protocols.
987821
Using Clustering Information for Sensor Network Localization
Sensor network localization continues to be an important research challenge. The goal of localization is to assign geographic coordinates to each node in the sensor network. Localization schemes for sensor network systems should work with inexpensive off-the-shelf hardware, scale to large networks, and also achieve good accuracy in the presence of irregularities and obstacles in the deployment area. 
 
We present a novel approach for localization that can satisfy all of these desired properties. Recent developments in sensor network clustering algorithms have resulted in distributed algorithms that produce highly regular clusters. We propose to make use of this regularity to inform our localization algorithm. The main advantages of our approach are that our protocol requires only three randomly-placed nodes that know their geographic coordinates, and does not require any ranging or positioning equipment (i.e., no signal strength measurement, ultrasound ranging, or directional antennas are needed). So far, only the DV-Hop localization mechanism worked with the same assumptions [1]. We show that our proposed approach may outperform DV-Hop in certain scenarios, in particular when there exist large obstacles in the deployment field, or when the deployment area is free of obstacles but the number of anchors is limited.
1655475
On the distribution and revocation of cryptographic keys in sensor networks
Key management has two important aspects: key distribution, which describes how to disseminate secret information to the principals so that secure communications can be initiated, and key revocation, which describes how to remove secrets that may have been compromised. Key management in sensor networks face constraints of large scale, lack of a priori information about deployment topology, and limitations of sensor node hardware. While key distribution has been studied extensively in recent works, the problem of key and node revocation in sensor networks has received relatively little attention. Yet, revocation protocols that function correctly in the presence of active adversaries pretending to be legitimate protocol participants via compromised sensor nodes are essential. In their absence, an adversary could take control of the sensor network's operation by using compromised nodes which retain their network connectivity for extended periods of time. In this paper, we present an overview of key-distribution methods in sensor networks and their salient features to provide context for understanding key and node revocation. Then, we define basic properties that distributed sensor-node revocation protocols must satisfy and present a protocol for distributed node revocation that satisfies these properties under general assumptions and a standard attacker model.
9263402
Artificial convolution neural network for medical image pattern recognition
Abstract We have developed several training methods in conjunction with a convolution neural network for general medical image pattern recognition. An unconventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. The structure of the artificial neural network is a simplified network structure of the neocognitron. Two-dimensional local connection as a group is the fundamental architecture for the signal propagation in the convolution neural network. Weighting coefficients of convolution kernels are formed by the neural network through backpropagated training for this artificial neural net. In addition, radiologists' reading procedure was modelled in order to instruct the artificial neural network to recognize the predefined image patterns and those of interest to experts. Our training techniques involve (a) radiologists' rating for each suspected image area, (b) backpropagation of generalized distribution, (c) trainer imposed functions, (d) shift and rotation invariance of diagnosis interpretation, and (e) consistency of clinical input data using appropriate background reduction functions. We have tested these methods for detecting lung nodules on chest radiographs and microcalcications on mammograms. The performance studies have shown the potential use of this technique in a clinical environment. We also used a profile double-matching technique for initial nodule search and used a wavelet high pass filtering technique to enhance subtle clustered microcalcifications. We set searching parameters at a highly sensitive level to identify all potential disease areas. The artificial convolution neural network acts as a final detection classifier to determine whether a disease pattern is shown on the suspected image area.
8031191
Dynamic dictionary matching and compressed suffix trees
Recent breakthrough in compressed indexing data structures has reduced the space for indexing a text (or a collection of texts) of length <i>n</i> from <i>O</i>(<i>n</i> log <i>n</i>) bits to <i>O</i>(<i>n</i>) bits, while allowing very efficient pattern matching [10, 13]. Yet the compressed nature of such indices also makes them difficult to update dynamically. This paper presents the first <i>O</i>(<i>n</i>)-bit representation of a suffix tree for a dynamic collection of texts whose total length is <i>n</i>, which supports insertion and deletion of a text <i>T</i> in <i>O</i>(|<i>T</i> | log<sup>2</sup> <i>n</i>) time, as well as all suffix tree traversal operations, including forward and backward suffix links. This work can be regarded as a generalization of the compressed representation of static texts. Our new suffix tree representation serves as a core part in a compact solution for the dynamic dictionary matching problem, i.e., providing an <i>O</i>(<i>d</i>)-bit data structure for a dynamic collection of patterns of total length <i>d</i> that can support the dictionary matching query efficiently. When compared with the <i>O</i>(<i>d</i> log <i>d</i>)-bit suffix tree based solution of Amir <i>et al.</i>, the compact solution increases the query time by roughly a factor of log <i>d</i> only. In the study of the above results, we also derive the first <i>O</i>(<i>n</i>)-bit representation for maintaining <i>n</i> pairs of balanced parentheses in <i>O</i>(log <i>n</i>/log log <i>n</i>) time per operation, matching the time complexity of the previous <i>O</i>(<i>n</i> log <i>n</i>)-bit solution.