On the basis of our previous research work, we have improved the core algorithm for global model checking of propositional µ-Calculus. While the improved algorithm has the same theoretical time and space complexi...
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A common assumption in the design and analysis of many MIMO transmission schemes is the so-called Kronecker model. This model is often a crucial step to obtain mathematically tractable solutions, but has also been cri...
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We study the problem of computing personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a dataset that contains the submitted bids of n...
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ISBN:
(纸本)9781450367929
We study the problem of computing personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a dataset that contains the submitted bids of n buyers in a set of auctions and the goal is to return personalized reserve prices r that maximize the revenue earned on these auctions by running eager second price auctions with reserve r. We present a novel LP formulation to this problem and a rounding procedure which achieves a (1+2(√2-1)e√2-2)-1≅0.684-approximation. This improves over the 1/2-approximation algorithm due to Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which bounds the performance of any algorithm based on this LP.
Networks are enablers, allowing the diffusion of valuable information. But just as a network is a conduit for valuable information, so it is for misinformation. One major challenge in a networked environment is limiti...
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ISBN:
(纸本)9781450383073
Networks are enablers, allowing the diffusion of valuable information. But just as a network is a conduit for valuable information, so it is for misinformation. One major challenge in a networked environment is limiting the spread of misinformation. Consider a large online social network, such as Twitter. Unethical users spread anti-social posts, which negatively affect other users and damage community dynamics[6], fraudsters send spam and phishing emails that threaten people's financial security[7], accounts occupied by malicious parties spread toxic information (e.g., hate speech, fake news), stirring up controversy and manipulating political views among social network users[1], fake reviews posted by bots mislead consumers' decision making[15], etc. An intuitive idea to limit the spread of misinformation is removing malicious nodes from networks, for example, terminate accounts on Twitter that spread spam. Importantly, a principled method to decide which nodes to remove from a network has wide applications; in the case of infectious disease, the inoculation of a group of people is essentially "removing" them from the contagion network[2,5,8,12,16-19]. A critical observation is that the loss associated with a decision whether to remove a node depends both on the node's likelihood of being malicious and its local network structure. Consequently, the typical approach in which we simply classify nodes as malicious or benign using a threshold on the associated maliciousness probability[10] is inadequate, as it fails to account for network consequences of such decisions. Rather, the problem is fundamentally about choosing which subset of nodes to remove, as decisions about removing individual nodes are no longer independent. We developed a model that provides decisions about which nodes to remove[20]. The model considers both the likelihood of nodes being malicious and their local network structures. Several algorithmic insights are derived from studying the model, inc
The existence of hidden and exposed nodes can have a significant negative impact on the performance of IEEE802.11 networks. Such nodes can increase the probability of collisions and limit the spatial reuse on the chan...
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A huge amount of data is generated in power system every year, which covers the information in different aspects such as grid production, operation, customer service and maintenance, reflecting the actual production a...
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In this article a new algorithm for the design of stationary input sequences for system identification is presented. The stationary input signal is generated by optimizing an approximation of a scalar function of the ...
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ISBN:
(纸本)9781467357159
In this article a new algorithm for the design of stationary input sequences for system identification is presented. The stationary input signal is generated by optimizing an approximation of a scalar function of the information matrix, based on stationary input sequences generated from prime cycles, which describe the set of finite Markov chains of a given order. This method can be used for solving input design problems for nonlinear systems. In particular it can handle amplitude constraints on the input. Numerical examples show that the new algorithm is computationally attractive and that is consistent with previously reported results.
With the rapid growth of on-demand taxi services, like Uber, Lyft, etc., urban public transportation scenario is shifting towards a personalized transportation choice for most commuters. While taxi rides are comfortab...
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With the rapid growth of on-demand taxi services, like Uber, Lyft, etc., urban public transportation scenario is shifting towards a personalized transportation choice for most commuters. While taxi rides are comfortable and time efficient, they often lead to higher cost and road congestion due to lower overall occupancy than bigger vehicles. A possible solution to improve taxi occupancy is to adopt ride sharing. Existing ride sharing solutions are mostly centralized and proprietary. How- ever, given the wide spatio-temporal variation of incoming ride requests designing a dynamic and distributed shared-ride scheduling system is NP-hard. In this thesis, we have proposed a publisher (passengers) and subscriber (taxis) based ride sharing system that provides effective real-time ride scheduling for multiple passengers willing to share rides in part or in full. A particle swarm based route optimization strategy has been applied to determine the most preferable route for passengers. Empirical analysis using large scale single-user taxi ride records from Chicago Transit Authority, show that, our proposed system, ensures a maximum of 91.74% and 63.29% overall success rates during peak and non-peak hours, respectively.
In China, the number of the power station increases quickly. According to the information from the Internet, From 2001 to August 2005, State Environmental Protection Administration has approved 472 thermal power proje...
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In China, the number of the power station increases quickly. According to the information from the Internet, From 2001 to August 2005, State Environmental Protection Administration has approved 472 thermal power projects, and the installed capacity is as much as 344382 *** which, 135 thermal power projects are approved in 2001, the installed capacity is as much as 107590 MW, 207% more than last year;213 thermal power projects are approved From January to August in 2005, the installed capacity is as much as 168546 MW, 420% more than last *** these power stations all come online, Chinese installed capacity will be as much as 5.82 billion KW, 145% more than 2000. In December 2006, the growth of the power stations keeps high *** same month, the whole country’s generating capacity was 2266 billion kilowatt-hour, 15.5% more than last *** the rise of the winter electricity consumption, the generating capacity of power projects increased *** genearating capacity of power projects in December was 10.9% more than last *** January to December in 2006 the whole country’s generating capacity was 23009 billion kilowatt-hour, 15.08% more than last *** January to October in 2007 the whole country’s generating capacity was 21756 billion kilowatt-hour, 16.04% more than last *** generating capacity in August had the largest number, which was 2390 billion kilowatt-hour, 10.19% more than last year.
Online propaganda embodies a potent new form of warfare; one that extends the strategic reach of our adversaries and overwhelms analysts. Foreign organizations have effectively leveraged an online presence to influenc...
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Online propaganda embodies a potent new form of warfare; one that extends the strategic reach of our adversaries and overwhelms analysts. Foreign organizations have effectively leveraged an online presence to influence elections and distance-recruit. The Islamic State has also shown proficiency in outsourcing violence, proving that propaganda can enable an organization to wage physical war at very little cost and without the resources traditionally required. To augment new counter foreign propaganda initiatives, this thesis presents a pipeline for defining, detecting and monitoring ideology in text. A corpus of 3,049 modern online texts was assembled and two classifiers were created: one for detecting authorship and another for detecting ideology. The classifiers demonstrated 92.70% recall and 95.84% precision in detecting authorship, and detected ideological content with 76.53% recall and 95.61% precision. Both classifiers were combined to simulate how an ideology can be detected and how its composition could be passively monitored across time. Implementation of such a system could conserve manpower in the intelligence community and add a new dimension to analysis. Although this pipeline makes presumptions about the quality and integrity of input, it is a novel contribution to the fields of Natural Language Processing and Information Warfare.
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