An evaluation study of clustering algorithms in the scope of user communities assessment

AbstractIn this paper, we provide the results of ongoing work in Magnet Beyond project, regarding social networking services. We introduce an integrated social networking framework through the definition or the appropriate notions and metrics. This allows one to run an evaluation study of three widely used clustering methods (k-means, hierarchical and spectral clustering) in the scope of social groups assessment and in regard to the cardinality of the profile used to assess users’ preferences. Such an evaluation study is performed in the context of our service requirements (i.e. on the basis of equal-sized group formation and of maximization of interests’ commonalities between users within each social group). The experimental results indicate that spectral clustering, due to the optimization it offers in terms of normalized cut minimization, is applicable within the context of Magnet Beyond socialization services. Regarding profile’s cardinality impact on the system performance, this is shown to be highly dependent on the underlying distribution that characterizes the frequency of user preferences appearance. Our work also incorporates the introduction of a heuristic algorithm that assigns new users that join the service into appropriate social groups, once the service has been initialized and the groups have been assessed using spectral clustering. The results clearly show that our approach is able to adhere to the service requirements as new users join the system, without the need of an iterative spectral clustering application that is computationally demanding

A Neural Network-Genetic Algorithm Scheme for Optimal Grouping of Individual Cores in Three-Phase Distributed Transformers

This paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network-genetic algorithm approach. The originality of the work presented in this paper is that it tackles the iron loss reduction problem during the transformer production phase, while previous works were concentrated on the design phase. More specifically, neural networks effectively use measurements taken at the first stages of core construction in order to predict the iron losses of the assembled transformers, while genetic algorithms are used to improve the grouping process of the individual cores by reducing iron losses of assembled transformers. The proposed method has been tested on a real transformer manufacturing industry and has resulted in a significant cost reduction

Stochastic Search Algorithms for Optimal Content-Based Sampling Of Video Sequences

A video content representation framework is proposed in this paper, for extracting limited, but meaningful, information of video data, directly from the MPEG compressed domain. In particular, extraction of several representative shots is performed for each video sequence in a content based rate sampling framework. An approach, based on minimization of a cross-correlation criterion of the video frames has been adopted for the shot selection. For efficient implementation of the latter approach, a logarithmic search in a stochastic framework is proposed. The method always converges to the global minimum as is proven in the paper

An Adaptable Neural-Network Model for Recursive Nonlinear Traffic Prediction and Modeling of MPEG Video Sources

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neuralnetwork architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better perform..

On-line retrainable neural networks: improving the performance of neural networks in image analysis problems

Abstract—A novel approach is presented in this paper for improving the performance of neural-network classifiers in image recognition, segmentation, or coding applications, based on a retraining procedure at the user level. The procedure includes: 1) a training algorithm for adapting the network weights to the current condition; 2) a maximum a posteriori (MAP) estimation procedure for optimally selecting the most representative data of the current environment as retraining data; and 3) a decision mechanism for determining when network retraining should be activated. The training algorithm takes into consideration both the former and the current network knowledge in order to achieve good generalization. The MAP estimation procedure models the network output as a Markov random field (MRF) and optimally selects the set of training inputs and corresponding desired outputs. Results are presented which illustrate the theoretical developments as well as the performance of the proposed approach in real-life experiments. Index Terms—Image analysis, MPEG-4, neural-network retraining, segmentation, weight adaptation

Content-based Decomposition of Gesture Videos

Abstract- In this paper we present a novel method for gesture video decomposition based on the depicted content. From the initial content the key-frames are extracted and the neighboring frames are assigned to key–frames of similar content. The resulting frame groups are decomposed to binary trees, based on the energy of the depicted gestures. In case of reduced bandwidth we keep the original timeline but we send only a dynamically adapted video summary. The respective frames are obtained by moving appropriately across the hierarchy layers of the constructed tree. The hierarchically structured video can be used for purposes such as efficient video browsing and transmission of dynamically generated summaries over low bandwidth networks, for communication or human-computer interface applications.

Recursive Non Linear Models for On Line Traffic Prediction of VBR MPEG Coded Video Sources

Any performance evaluation of broadband networks requires modeling of the actual network traffic. Since multimedia services and especially MPEG coded video streams are expected to be a major traffic component over these networks, modeling of such services and traffic prediction are useful for the reliable operation of the broadband based an Asynchronous Transfer Mode (ATM) networks. In this paper, a Recursive implementation of a Non linear AutoRegressive model (RNAR) is presented for on line traffic prediction of Variable Bit Rate (VBR) MPEG-2 video sources. This is accomplished by using an efficient weight adaptation algorithm so that the network provide good performance even in case of highly fluctuated traffic rates. In particular, the network weights are adapted so that the output is approximately equal to the current data while preserving the former knowledge of the network. Experimental results are presented to show the good performance of the proposed scheme. Furthermore, comparison with other linear or non linear techniques is presented to show that the adopted method yields better results than the other ones