Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

/pdf/maximizing-the-spread-of-influence-through-a-social-network-47y1ehuvwi.pdf

Maximizing the Spread of Influence through a Social Network

Matrix Factorization Techniques for Recommender Systems

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Du%20-%20Automatic%20License%20Plate%20Recognition%20-%202013.pdf

Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them Issues such as processing time, computational power, and recognition rate are also addressed, when available Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment

/pdf/license-plate-recognition-from-still-images-and-video-njnnbay5h5.pdf

License Plate Recognition From Still Images and Video Sequences: A Survey

We study the optimal pricing strategies of a monopolist selling a divisible good (service) to consumers who are embedded in a social network A key feature of our model is that consumers experience a (positive) local network effect In particular, each consumer's usage level depends directly on the usage of her neighbors in the social network structure Thus, the monopolist's optimal pricing strategy may involve offering discounts to certain agents who have a central position in the underlying network Our results can be summarized as follows First, we consider a setting where the monopolist can offer individualized prices and derive a characterization of the optimal price for each consumer as a function of her network position In particular, we show that it is optimal for the monopolist to charge each agent a price that consists of three components: (i) a nominal term that is independent of the network structure, (ii) a discount term proportional to the influence that this agent exerts over the rest of the social network (quantified by the agent's Bonacich centrality), and (iii) a markup term proportional to the influence that the network exerts on the agent In the second part of the paper, we discuss the optimal strategy of a monopolist who can only choose a single uniform price for the good and derive an algorithm polynomial in the number of agents to compute such a price Third, we assume that the monopolist can offer the good in two prices, full and discounted, and we study the problem of determining which set of consumers should be given the discount We show that the problem is NP-hard; however, we provide an explicit characterization of the set of agents who should be offered the discounted price Next, we describe an approximation algorithm for finding the optimal set of agents We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88% of the optimal profit Finally, we highlight the value of network information by comparing the profits of a monopolist who does not take into account the network effects when choosing her pricing policy to those of a monopolist who uses this information optimally

/pdf/optimal-pricing-in-networks-with-externalities-487gf6sdlv.pdf

Optimal Pricing in Networks with Externalities

We give approximation algorithms and inapproximability results for a class of movement problems. In general, these problems involve planning the coordinated motion of a large collection of objects (representing anything from a robot swarm or firefighter team to map labels or network messages) to achieve a global property of the network while minimizing the maximum or average movement. In particular, we consider the goals of achieving connectivity (undirected and directed), achieving connectivity between a given pair of vertices, achieving independence (a dispersion problem), and achieving a perfect matching (with applications to multicasting). This general family of movement problems encompasses an intriguing range of graph and geometric algorithms, with several real-world applications and a surprising range of approximability. In some cases, we obtain tight approximation and inapproximability results using direct techniques (without use of PCP), assuming just that P ≠ NP.

Minimizing movement

We study Nash equilibria in the setting of network creation games introduced recently by Fabrikant, Luthra, Maneva, Papadimitriou and Shenker. In this game we have a set of selfish node players, each creating some incident links, and the goal is to minimize α times the cost of the created links plus sum of the distances to all other players. Fabrikant et al. proved an upper bound O(√α) on the price of anarchy, i.e., the relative cost of the lack of coordination. Albers, Eilts, Even-Dar, Mansour, and Roditty show that the price of anarchy is constant for α = O(√n) and for α ≥ 12n[lg n], and that the price of anarchy is 15(1+min {α2n, n2α})1/3) for any α. The latter bound shows the first sublinear worst-case bound, O(n1/3), for all α. But no better bound is known for α between ω(√n) and o(n lg n). Yet α ≈ n is perhaps the most interesting range, for it corresponds to considering the average distance (instead ofthe sum of distances) to other nodes to be roughly on par with link creation (effectively dividing α by n).In this paper, we prove the first o(ne) upper bound for general α, namely 2O(√lg n). We also prove aconstant upper bound for α = O(n1-e) for any fixed e > 0, substantially reducing the range of α for which constant bounds have not been obtained. Along the way, we also improve the constant upper bound by Albers et al. (with the leadconstant of 15 ) to 6 for α n, we prove that the price of anarchy is Θ(n/√ α).Finally we introduce a variant of both network creation games, in which each player desires to minimize α times the cost of its created links plus the maximum distance (instead of the sum of distances) to the other players. This variant of the problem is naturally motivated by considering the worst case instead of the average case. Interestingly, for the original (unilateral) game, we show that the price of anarchy is at most 2 for α ≥ n, O(min{4√lg n, (n/α)1/3}) for 2√lgn ≤ α ≤ n, and O(n2/α) for α α+1) for α ≤ n, and an upper bound of 2 for α > n.

/pdf/the-price-of-anarchy-in-network-creation-games-3k86wol14v.pdf

The price of anarchy in network creation games

This paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. The proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. It extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. We have formed a rather complete database of 269 images in different conditions. The proposed algorithm successfully detecteds the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature.

/pdf/an-efficient-features-based-license-plate-localization-39op4k1hqx.pdf

An Efficient Features - Based License Plate Localization Method

We study the optimal pricing for revenue maximization over social networks in the presence of positive network externalities. In our model, the value of a digital good for a buyer is a function of the set of buyers who have already bought the item. In this setting, a decision to buy an item depends on its price and also on the set of other buyers that have already owned that item. The revenue maximization problem in the context of social networks has been studied by Hartline, Mirrokni, and Sundararajan [4], following the previous line of research on optimal viral marketing over social networks [5,6,7].

We consider the Bayesian setting in which there are some prior knowledge of the probability distribution on the valuations of buyers. In particular, we study two iterative pricing models in which a seller iteratively posts a new price for a digital good (visible to all buyers). In one model, re-pricing of the items are only allowed at a limited rate. For this case, we give a FPTAS for the optimal pricing strategy in the general case. In the second model, we allow very frequent re-pricing of the items. We show that the revenue maximization problem in this case is inapproximable even for simple deterministic valuation functions. In the light of this hardness result, we present constant and logarithmic approximation algorithms when the individual distributions are identical.

/pdf/optimal-iterative-pricing-over-social-networks-555a0h2co2.pdf

Optimal iterative pricing over social networks

We study Nash equilibria in the setting of network creation games introduced recently by Fabrikant, Luthra, Maneva, Papadimitriou, and Shenker. In this game we have a set of selfish node players, each creating some incident links, and the goal is to minimize α times the cost of the created links plus sum of the distances to all other players. Fabrikant et al. proved an upper bound O(√α) on the price of anarchy: the relative cost of the lack of coordination. Albers, Eilts, Even-Dar, Mansour, and Roditty show that the price of anarchy is constant for α = O(√n) and for α g 12 n ⌈ lg n ⌉, and that the price of anarchy is 15(1+(min{α2/n, n2/α})1/3) for any α. The latter bound shows the first sublinear worst-case bound, O(n1/3), for all α. But no better bound is known for α between ω(√ n) and o(n lg n).Yet α a n is perhaps the most interesting range, for it corresponds to considering the average distance (instead of the sum of distances) to other nodes to be roughly on par with link creation (effectively dividing α by n). In this article, we prove the first o(ne) upper bound for general α, namely 2O(√ lg n). We also prove a constant upper bound for α = O(n1-e) for any fixed e > 0, substantially reducing the range of α for which constant bounds have not been obtained. Along the way, we also improve the constant upper bound by Albers et al. (with the lead constant of 15) to 6 for α n, we prove that the price of anarchy is Θ(n/√ α).Finally we introduce a variant of both network creation games, in which each player desires to minimize α times the cost of its created links plus the maximum distance (instead of the sum of distances) to the other players. This variant of the problem is naturally motivated by considering the worst case instead of the average case. Interestingly, for the original (unilateral) game, we show that the price of anarchy is at most 2 for α g n, O(min {4√lg n, (n/α)1/3}) for 2√ lg n l α l n, and O(n2/α) for α n.

/pdf/the-price-of-anarchy-in-network-creation-games-484rxnde2g.pdf

Hamid Mahini

Papers

Minimizing movement

The price of anarchy in network creation games

An Efficient Features - Based License Plate Localization Method

Optimal iterative pricing over social networks

The price of anarchy in network creation games