Sharif Digital Repository

The KASUMI block cipher is used for the cellular communications networks and safety of many wireless standards. Third generation cellular network technology (3G) permits to transmit information, voice and video at very high data rates never seen before that will revolutionize personal communications and information exchange. On the other hand, Residue Number System (RNS) is a modular representation and is evidenced to be serviceable equipment in many applications which need high-speed computations and high-performance components. RNS is a non-weighted and integer number system that can support secure, highspeed, low-power, parallel and carry-free arithmetic. For attaining the most... 

Third generation cellular network technology (3G) can revolutionize communications and data exchanges between many people in a more overwhelming fashion than 2G and 2.5G networks did. The 3G UMTS, the 3G GSM and the 3G GPRS rely on the KASUMI block cipher. Therefore, increasing speed, decreasing power consumption and error detection/correction are the major concerns of the KASUMI algorithm and its generation. On the other hand, Residue Number System is a non-weighted number system and it is currently considered as an important method for high-speed, low-power, parallel and carry-free arithmetic realizations. Redundant Residue Number System is an extension of RNS that also supports error... 

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... 

In analysing the structural organization of a social network, identifying important nodes has been a fundamental problem. The concept of network centrality deals with the assessment of the relative importance of a particular node within the network. Most of the traditional network centrality definitions have a high computational cost and require full knowledge of network topological structure. On the one hand, in many applications we are only interested in detecting the top-k central nodes of the network with the largest values considering a specific centrality metric. On the other hand, it is not feasible to efficiently identify central nodes in a large real-world social network via... 

We overview the main results in Compressed Sensing and Social Networks, and discuss the impact they have on Cyber Physical Social Systems (CPSS), which are currently emerging on top of the Internet of Things. Moreover, inspired by randomized Gossip Protocols, we introduce TopGossip, a new compressed-sensing algorithm for the prediction of the top-k most influential nodes in a social network. TopGossip is able to make this prediction by sampling only a relatively small portion of the social network, and without having any prior knowledge of the network structure itself, except for its set of nodes. Our experimental results on three well-known benchmarks, Facebook, Twitter, and Barabási,... 

Controlling networked dynamical systems is a complex endeavor, specifically keeping in mind the fact that in many scenarios the actors that are engaged in the dynamism behave selfishly and therefore only take into account their own individual utility, a setting that has been widely studied in the field of game theory. One way that we can control the system dynamics is through the use of control parameters that are at our disposal, but finding optimal values for these parameters is a complex and time consuming task. In this paper we use the relation between network structural properties and control parameters to create a mathematical model that uses learning methods to find optimal values... 

In this paper, motivated by network inference, we introduce a general framework, called LSR-Weighted, to efficiently recover sparse characteristic of links in weighted networks. The links in many real-world networks are not only binary entities, either present or not, but rather have associated weights that record their strengths relative to one another. Such models are generally described in terms of weighted networks. The LSR-Weighted framework uses a newly emerged paradigm in sparse signal recovery named compressive sensing. We study the problem of recovering sparse link vectors with network topological constraints over weighted networks. We evaluate performance of the proposed framework... 

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users'... 

Recommender Systems have become an attractive field within the recent decade because they facilitate users' selection process within limited time. Conventional recommender systems have proposed numerous methods focusing on recommendations to individual users. Recently, due to a significant increase in the number of users, studies in this field have shifted to properly identify groups of people with similar preferences and provide a list of recommendations for each group. Offering a recommendations list to each individual requires significant computational cost and it is therefore often not efficient. So far, most of the studies impose four restrictive assumptions: (1) limited number of... 

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users'... 

This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in communication networks, and the connector hubs in biological systems. In this paper, we introduce DICeNod, a new approach to efficiently identify central nodes in social networks without direct measurement of each individual node using compressive sensing, which is a well-known paradigm in sparse... 

Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top-k betweenness centrality nodes in networks, using compressive sensing.... 

Passive radars use illuminators of opportunity to detect target and measure its parameters. The usual processing routine consists of direct path interference and clutter removal and computation of cross ambiguity function (CAF) to estimate ampIitude-range-Doppler surface. Main parameters affecting this procedure are direct path interference and clutter, receiver thermal noise and near large targets. This paper presents an investigation on effect of each parameter and tries to estimate processing gain of each Block. A comparison between different adaptive interference removal methods also presented  

Herein, we demonstrated a facile method for the fabrication of magnetic and superhydrophobic polyurethane sponge with water contact angle of 159° as an adsorbent for cleanup the marine oil spill pollution. For this aim, a polyurethane sponge was coated with carbon black (CB), hexagonal boron nitride (h-BN)@Fe3O4, and acrylic resin and then characterized by different techniques. Owing to the chemical and thermal stability of h-BN and CB, the modified sponge was stable under corrosive conditions (pH = 1–14 and salt solutions) and at different temperatures (−12 °C–105 °C). In addition to common oils and organic solvents, we also used the real spilled oils containing monoaromatics and... 

The rapid and effective separation of high-viscosity heavy crude oil from seawater is a worldwide challenge. Herein, an ultralow density, photothermal, superhydrophobic, and thermally conductive polyurethane/polyaniline/hexagonal boron nitride@Fe3O4/polyacrylic-oleic acid resin sponge (PU/PANI/h-BN@Fe3O4/AR) was fabricated with a water contact angle (WCA) of 158°, thermal conductivity of 0.76 W m-1 K-1, density of 0.038 g cm-3, limited oxygen index (LOI) of 28.82%, and porosity of 97.97% and used for solar-assisted separation of high-viscosity crude oil from water. Photothermal components were composed of PANI and Fe3O4, while h-BN particles were used as thermally conductive and flame... 

Herein, a series of superhydrophobic thin polyacrylic resin-coated carbon black (CB)/hexagonal boron nitride (h-BN)@Fe3O4/cellulose composite papers with good flexibility, low density (~0.67 g/cm3), high electrical conductivity (~0.065 S/cm), good thermal conductivity (0.462 W.m−1. K−1), and with water contact angle (WCA) of 153° were successfully fabricated by a facile dip-coating/spraying method. The CB-BN@Fe3O4 distribution in cellulose matrix provided high electrical conductivity in the in-plane and thickness directions. The electrical conductivity in both in-plane and thickness directions increased by increasing the number of vacuum-assisted dip-coating cycles. Moreover, these...