8605759
Fine-grained contract negotiation for hierarchical software components
Component-based development addresses the complexity of large applications by building software systems from reusable software components. To support reuse and successfully combine units of software, the contractual approach turns out to be well-suited to specify and to verify components and their interactions. However, as contracts must support functional and extra-functional aspects, they are frequently challenged by fluctuations in extra-functional properties and dynamic reconfigurations of components. In this paper, we propose a negotiation model in which components have clearly identified roles and interact in order to automatically restore the validity of contracts. The negotiation model currently supports a concession-based negotiation policy and is well-suited to behavioral contracts based on executable assertions. This model is integrated into ConFract, a contracting system for the fractal hierarchical component model.
1945331
From Components to Autonomic Elements Using Negotiable Contracts
Autonomic computing aims at producing software systems that can manage themselves. As component-based development also partly addresses the complexity of large applications, we propose to combine the benefits of both approaches by using components equipped with negotiable contracts. These contracts specify the correct behavior of the components and play a central role in feedback control loops to enforce some autonomic features on components. In this paper, we present ConFract, a contract-based framework for hierarchical components in which contracts are runtime objects that are dynamically built from specifications, and automatically updated according to dynamic reconfigurations. Moreover, contracts clearly define the responsibilities (guarantor, beneficiaries) between their participating components which are exploited by some negotiations to automatically adapt components and contracts, and revalidate the system. The generic negotiation mechanism and an associated concession-based policy are presented.
4841642
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a linear transformation. In this paper, we propose a new metric learning method that performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present three methods for solving it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some UCI data sets. Besides applying LLMA to semi-supervised clustering, we have also used it to improve the performance of content-based image retrieval systems through metric learning. Experimental results based on two real-world image databases show that LLMA significantly outperforms other methods in boosting the image retrieval performance.
14216000
Robust locally linear embedding
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community. These methods are promising in that they can automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, using tractable linear algebraic techniques that are easy to implement and are not prone to local minima. Despite their appealing properties, these NLDR methods are not robust against outliers in the data, yet so far very little has been done to address the robustness problem. In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust. We refer to this approach as robust locally linear embedding (RLLE). We also present several specific methods for realizing this general RLLE approach. Experimental results on both synthetic and real-world data show that RLLE is very robust against outliers.
8902172
Kernel-based distance metric learning for content-based image retrieval
For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system depends critically on the similarity or dissimilarity measure used. Instead of manually choosing a distance function in advance, a more promising approach is to learn a good distance function from data automatically. In this paper, we propose a kernel approach to improve the retrieval performance of CBIR systems by learning a distance metric based on pairwise constraints between images as supervisory information. Unlike most existing metric learning methods which learn a Mahalanobis metric corresponding to performing linear transformation in the original image space, we define the transformation in the kernel-induced feature space which is nonlinearly related to the image space. Experiments performed on two real-world image databases show that our method not only improves the retrieval performance of Euclidean distance without distance learning, but it also outperforms other distance learning methods significantly due to its higher flexibility in metric learning.
16045709
A General Model and Analysis of Physical Layer Capture in 802.11 Networks
While packet capture has been observed in real implementations of 802.11 devices, there is a lack of accurate models that describe the phenomenon. We present a general analytical model and an iterative method that predicts error probabilities and throughputs of packet transmissions with multiple senderreceiver pairs. Our model offers a more accurate prediction than previous work by taking into account the cumulative strength of interference signals and using the BER model to convert a signal to interference and noise ratio value to a bit error probability. This permits the analysis of packet reception at any transmission rate with interference from neighbors at any set of locations. We also prove that our iterative method converges, and we verify the accuracy of our model through simulations in Qualnet. Last, we present a rate assignment algorithm to reduce the average delay as an application of our analysis.
11911656
802.11 Link Interference: A Simple Model and A Performance Enhancement
Recent findings have revealed that the carrier-sensing range set in current major implementations does not detect all interference signals in 802.11 networks. In this paper, we investigate the effect of interference and develop a mathematical model for it. The accuracy of our model is verified via simulations. Based on an insight from our model, we present a simple protocol that operates on the top of 802.11 MAC layer and achieves more throughput than rate-adjustment schemes.