Sharif Digital Repository

Partially-observed network data collected by link-tracing based sampling methods is often being studied to obtain the characteristics of a large complex network. However, little attention has been paid to sampling from directed networks such as WWW and Peer-to-Peer networks. In this paper, we propose a novel two-step (sampling/estimation) framework to measure nodal characteristics which can be defined by an average target function in an arbitrary directed network. To this end, we propose a personalized PageRank-based algorithm to visit and sample nodes. This algorithm only uses already visited nodes as local information without any prior knowledge about the latent structure of the network.... 

Graph based methods are among the most active and applicable approaches studied in semi-supervised learning. The problem of neighborhood graph construction for these methods is addressed in this paper. Neighborhood graph construction plays a key role in the quality of the classification in graph based methods. Several unsupervised graph construction methods have been proposed that have addressed issues such as data noise, geometrical properties of the underlying manifold and graph hyper-parameters selection. In contrast, in order to adapt the graph construction to the given classification task, many of the recent graph construction methods take advantage of the data labels. However, these... 

This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm. Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the... 

This letter presents a complex network model for the BitTorrent; the most popular peer-to-peer network. Three important topological characteristics; (i) degree distribution, (ii) clustering coefficient and (iii) average path length of this network are analytically modeled. The analytical computations are confirmed by simulations. Moreover, the accuracy of the proposed model was confirmed by exploiting a BitTorrent simulator  

In this letter, a spatially weighted sparse unmixing approach is proposed as a front-end for hyperspectral image classification using a linear SVM. The idea is to partition the pixels of a hyperspectral image into a number of disjoint spatial neighborhoods. Since neighboring pixels are often composed of similar materials, their sparse codes are encouraged to have similar sparsity patterns. This is accomplished by means of a reweighted ℓ1 framework where it is assumed that fractional abundances of neighboring pixels are distributed according to a common Laplacian Scale Mixture (LSM) prior with a shared scale parameter. This shared parameter determines which endmembers contribute to the group... 

Ambient Intelligence is the future of computing where devices predict what users need and help them carry out their everyday life activities easier. To make this prediction possible these environments should be aware of the context. Activity recognition is one of the most complex problems in context-aware environments. In this paper we propose a layered Dynamic Bayesian Network (DBN) to recognize activities in an oral presentation. The layered architecture gives us the opportunity to recognize complex activities using the classification results of sensory data in the first layer regardless of the physical environment. Our model is event-driven meaning the classification takes place only when... 

The peer to peer (P2P) file sharing applications have allocated a significant amount of today's Internet traffic. Among various P2P file sharing protocols, BitTorrent is the most common and popular one that attracts monthly a quarter of a billion users from all over the world. Similar to other P2P file sharing protocols, BitTorrent is mostly used for illegal sharing of copiright protected files such as movies, music and TV series. To impede this huge amount of illegal file distributions, anti-P2P companies have arisen to stand against these applications (specially the BitTorrent). To this end, they have begun to fire large-scale Internet attacks against BitTorrent networks. In this paper, we... 

In this paper a method for enhancing the synchronizability of dynamical networks is discussed. The method considers both local structural properties, i.e. node degrees, and global properties, i.e. node and edge betweenness centrality measures. Its performance is compared with that of some other heuristic methods and the evidence for its superior behavior is discussed. As an index for the synchronizability of a dynamical network, the eigenratio of the Laplacian matrix of the connection graph is considered. As network structures, scale-free, Watts- Strogatz and random networks of different sizes and topological properties are used. Numerical calculations of the eigenratio in the networks... 

In this paper, we study large-scale networks in terms of observability and controllability. In particular, we compare the number of unmatched nodes in two main types of Scale-Free (SF) networks: the Barabási-Albert (BA) model and the Holme-Kim (HK) model. Comparing the two models based on theory and simulation, we discuss the possible relation between clustering coefficient and the number of unmatched nodes. In this direction, we propose a new algorithm to reduce the number of unmatched nodes via link addition. The results are significant as one can reduce the number of unmatched nodes and therefore number of embedded sensors/actuators in, for example, an IoT network. This may significantly... 

While IEEE 802.16 standardizes the PHY and MAC layers of Wireless Metropolitan Area Networks (MAN), optimal scheduling which is crucial in providing QoS, remains to be an open issue. In this paper, we propose an optimization based uplink scheduling algorithm which aims at maximizing the throughput while guaranteeing the negotiated QoS parameters for all connections. Unlike typical strict priority schedulers the proposed scheduler is able to satisfy the requirements of higher priority connections while not letting the lower priority connections to be neglected. Additionally, a parameter is defined to ensure fairness among rtPS connections. The scheduler's performance is then evaluated through... 

This paper presents a new algorithm for detecting user-selected objects in a sequence of images based on a new weighted adaptive lifting scheme transform. In our algorithm, we first select a set of coefficients as object features in the wavelet transform domain and then build an adaptive transform considering the selected features. The goal of the designed adaptive transform is to "vanish" the selected features as much as possible in the transform domain. After applying both non-adaptive and adaptive transforms to a given test image, the corresponding transform domain coefficients are compared for detecting the object of interest. We have verified our claim with experimental results on 1-D... 

This paper presents a new adaptive lifting scheme transform for detecting user-selected objects in a sequence of images. In our algorithm, we first select a set of object features in the wavelet transform domain and then build an adaptive transform by using the selected features. The adaptive transform is constructed based on adaptive prediction in a lifting scheme procedure. Adaptive prediction is performed such that, the large coefficients in the high-pass component of the non-adaptive transform vanishes in the high-pass component of the adaptive transform. Finally, both the non-adaptive and adaptive transforms are applied to a given test image and the transform domain coefficients are... 

In this letter, we study large-scale networks in terms of observability and controllability. In particular, we compare the number of unmatched nodes in two main types of Scale-Free (SF) networks: the Barabási-Albert (BA) model and the Holme-Kim (HK) model. Comparing the two models based on theory and simulation, we discuss the possible relation between clustering coefficient and the number of unmatched nodes. In this direction, we propose a new algorithm to reduce the number of unmatched nodes via link addition. The results are significant as one can reduce the number of unmatched nodes and therefore number of embedded sensors/actuators in, for example, an IoT network. This may significantly... 

This paper presents a new algorithm for detecting user-selected objects in a sequence of images based on adaptively lifted wavelet transforms. In our algorithm, we first select a set of object features in the wavelet transform domain and then build a new transform by using the selected features. The new wavelet transform is constructed based on adaptive prediction in a lifting scheme structure. Adaptive prediction is performed such that, the large coefficients in the high-pass component of the old transform, vanish in the high-pass component of the new transform. Finally, both of the old and new transforms are applied to a given test image and the transform domain coefficients are compared... 

In this paper, a new on-line handwritten signature verification system using Hidden Markov Model (HMM) is presented. The proposed system segments each signature based on its perceptually important points and then computes for each segment a number of features that are scale and displacement invariant. The resulted sequence is then used for training an HMM to achieve signature verification. Our database includes 622 genuine signatures and 1010 forgery signatures that were collected from a population of 69 human subjects. Our verification system has achieved a false acceptance rate (FAR) of 4% and a false rejection rate (FRR) of 12%. © 2003 IEEE  

In this paper we propose a novel hierarchical Bayesian model for sparse regression problem to use in semi-supervised hyperspectral unmixing which assumes the signal recorded in each hyperspectral pixel is a linear combination of members of the spectral library contaminated by an additive Gaussian noise. To effectively utilizing the spatial correlation between neighboring pixels during the unmixing process, we exploit a Markov random field to simultaneously group pixels to clusters which are associated to regions with homogeneous mixtures in a natural scene. We assume Sparse fractional abundances of members of a cluster to be generated from an exponential distribution with the same rate... 

This paper surveys methods to aggregate redundant crowd labels in order to estimate unknown true labels. It presents a unified statistical latent model where the differences among popular methods in the field correspond to different choices for the parameters of the model. Afterward, algorithms to make inference on these models will be surveyed. Moreover, adaptive methods which iteratively collect labels based on the previously collected labels and estimated models will be discussed. In addition, this paper compares the distinguished methods and provides guidelines for future work required to address the current open issues.Recently, there has been a burst in the number of research projects... 

Packet loss recovery is an important part of P2P video streaming networks due to inevitable packet loss in today's internet and interdependency of data units in compressed video streams. In addition, the architecture of P2P streaming networks, in which the data delivered to the receivers through chain of peers, can intensify the impact of the internet packet loss on the quality of perceived video at the receivers. FEC and ARQ are the two most important techniques that can be used to overcome the side effect of the internet packet loss in P2P video streaming networks. Based on these two techniques, different packet loss recovery strategies can be applied in different overlay hops of a given... 

This paper addresses the problem of recovering sparse link vectors with network topological constraints that is motivated by network inference and tomography applications. We propose a novel framework called UCS-NT in the context of compressive sensing for sparse recovery in networks. In order to efficiently recover sparse specification of link vectors, we construct a feasible measurement matrix using this framework through connected paths. It is theoretically shown that, only O(k log(n)) path measurements are sufficient for uniquely recovering any k-sparse link vector. Moreover, extensive simulations demonstrate that this framework would converge to an accurate solution for a wide class of... 

Background: The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.Results: Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection...