A virtual backbone based approach for cooperative caching in mobile ad hoc networks

Cache look up is an integral part of cooperative caching in ad hoc networks. In this paper, we discuss a cooperative caching architecture with a distributed cache look up protocol which relies on a virtual backbone for locating and accessing data within a cooperate cache. Our proposal consists of two phases: (i) formation of a virtual backbone and (ii) the cache look up phase. The nodes in a Connected Dominating Set (CDS) form the virtual backbone. The cache look up protocol makes use of the nodes in the virtual backbone for effective data dissemination and discovery. The idea in this scheme is to reduce the number of nodes involved in cache look up process, by constructing a CDS that contains a small number of nodes, still having full coverage of the network. We evaluated the effect of various parameter settings on the performance metrics such as message overhead, cache hit ratio and average query delay. Compared to the previous schemes the proposed scheme not only reduces message overhead, but also improves the cache hit ratio and reduces the average delay

Weighted alpha-rate dominating sets in social networks

We are looking into variants of a domination set problem in social networks. While randomised algorithms for solving the minimum weighted domination set problem and the minimum alpha and alpha-rate domination problem on simple graphs are already present in the literature, we propose here a randomised algorithm for the minimum weighted alpha-rate domination set problem which is, to the best of our knowledge, the first such algorithm. A theoretical approximation bound based on a simple randomised rounding technique is given. The algorithm is implemented in Python and applied to a UK Twitter mentions networks using a measure of individuals’ influence (klout) as weights. We argue that the weights of vertices could be interpreted as the costs of getting those individuals on board for a campaign or a behaviour change intervention. The minimum weighted alpha-rate dominating set problem can therefore be seen as finding a set that minimises the total cost and each individual in a network has at least alpha percentage of its neighbours in the chosen set. We also test our algorithm on generated graphs with several thousand vertices and edges. Our results on this real-life Twitter networks and generated graphs show that the implementation is reasonably efficient and thus can be used for real-life applications when creating social network based interventions, designing social media campaigns and potentially improving users’ social media experience.

Improved flooding of broadcast messages using extended multipoint relaying

A common operation in wireless ad hoc networks is the flooding of broadcast messages to establish network topologies and routing tables. The flooding of broadcast messages is, however, a resource consuming process. It might require the retransmission of messages by most network nodes. It is, therefore, very important to optimize this operation. In this paper, we first analyze the multipoint relaying (MPR) flooding mechanism used by the Optimized Link State Routing (OLSR) protocol to distribute topology control (TC) messages among all the system nodes. We then propose a new flooding method, based on the fusion of two key concepts: distance-enabled multipoint relaying and connected dominating set (CDS) flooding. We present experimental simulationsthat show that our approach improves the performance of previous existing proposals.

Algorithmes heuristiques et exacts pour le problème de l’ensemble dominant connexe minimum.

Dans ce mémoire, nous abordons le problème de l’ensemble dominant connexe de cardinalité minimale. Nous nous penchons, en particulier, sur le développement de méthodes pour sa résolution basées sur la programmation par contraintes et la programmation en nombres entiers. Nous présentons, en l’occurrence, une heuristique et quelques méthodes exactes pouvant être utilisées comme heuristiques si on limite leur temps d’exécution. Nous décrivons notamment un algorithme basé sur l’approche de décomposition de Benders, un autre combinant cette dernière avec une stratégie d’investigation itérative, une variante de celle-ci utilisant la programmation par contraintes, et enfin une méthode utilisant uniquement la programmation par contraintes. Des résultats expérimentaux montrent que ces méthodes sont efficaces puisqu’elles améliorent les méthodes connues dans la littérature. En particulier, la méthode de décomposition de Benders avec une stratégie d’investigation itérative fournit les résultats les plus performants.

‘Studies on some topics in product graphs’

For routing problems in interconnection networks it is important to find the shortest containers between any two vertices, since the w-wide diameter gives the maximum communication delay when there are up to w−1 faulty nodes in a network modeled by a graph. The concept of ‘wide diameter’ was introduced by Hsu [41] to unify the concepts of diameter and The concept of ‘domination’ has attracted interest due to its wide applications in many real world situations [38]. A connected dominating set serves as a virtual backbone of a network and it is a set of vertices that helps in routing. In this thesis, we make an earnest attempt to study some of these notions in graph products. This include, the diameter variability, the diameter vulnerability, the component factors and the domination criticality.connectivity

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Efficient domination through eigenvalues

The paper begins with a new characterization of (k,τ)(k,τ)-regular sets. Then, using this result as well as the theory of star complements, we derive a simplex-like algorithm for determining whether or not a graph contains a (0,τ)(0,τ)-regular set. When τ=1τ=1, this algorithm can be applied to solve the efficient dominating set problem which is known to be NP-complete. If −1−1 is not an eigenvalue of the adjacency matrix of the graph, this particular algorithm runs in polynomial time. However, although it does not work in polynomial time in general, we report on its successful application to a vast set of randomly generated graphs.

Mathematical Programming Models for Influence Maximization on Social Networks

In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.

Finding the set of k-additive dominating measures viewed as a flow problem

n this paper we deal with the problem of obtaining the set of k-additive measures dominating a fuzzy measure. This problem extends the problem of deriving the set of probabilities dominating a fuzzy measure, an important problem appearing in Decision Making and Game Theory. The solution proposed in the paper follows the line developed by Chateauneuf and Jaffray for dominating probabilities and continued by Miranda et al. for dominating k-additive belief functions. Here, we address the general case transforming the problem into a similar one such that the involved set functions have non-negative Möbius transform; this simplifies the problem and allows a result similar to the one developed for belief functions. Although the set obtained is very large, we show that the conditions cannot be sharpened. On the other hand, we also show that it is possible to define a more restrictive subset, providing a more natural extension of the result for probabilities, such that it is possible to derive any k-additive dominating measure from it.

Thoracic Manifestations of Connective Tissue Diseases

Connective tissue diseases (CTDs) comprise several immunologic systemic disorders, each of which associated with a particular set of clinical manifestations and autoimmune profile. CTDs may cause numerous thoracic abnormalities, which vary in frequency and pattern according to the underlying disorder. The CTDs that most commonly involve the respiratory system are progressive systemic sclerosis, systemic lupus erythematosus, rheumatoid arthritis, Sjögren syndrome, polymyositis, dermatomyositis, and mixed connective tissue disease. Pulmonary abnormalities in this group of patients may result from CTD-related lung disease or treatment complications, namely drug toxicity and opportunistic infections. The most important thoracic manifestations of CTDs are interstitial lung disease and pulmonary arterial hypertension, with nonspecific interstitial pneumonia being the most common pattern of interstitial lung disease. High-resolution computed tomography is a valuable tool in the initial evaluation and follow-up of patients with CTDs. As such, general knowledge of the most common high-resolution computed tomographic features of CTD-related lung disease allows the radiologist to contribute to better patient management.

Response of mice connective tissue to intracanal dressings containing chlorhexidine

Substances containing chlorhexidine (CHX) have been studied as intracanal medicaments. The aim of the present study was to characterize the response of mouse subcutaneous connective tissue to CHX-containing medications by conventional optical microscopy. The tissue response was evaluated by implanting polyethylene tubes containing one of the substances evaluated: Calen paste + 0.5% CHX, Calen + 2% CHX, 2% CHX gel, and Calen paste (control). After experimental periods of 7, 21, and 63 days, the implants (n = 10) were removed along with the subcutaneous connective tissue. Tissue samples were subjected to histological processing, and sections were stained with hematoxylin and eosin. Qualitative and quantitative analyses of the number of inflammatory cells, blood vessels, and vascularized areas were performed. Results were analyzed by ANOVA and Tukey tests with the significance level set at 5%. We concluded that Calen + 0.5% CHX led to reparative tissue response in contrast with Calen + 2% CHX and 2% CHX gel, which induced persistent inflammatory response, pointing to the aggressive nature of this mixture. When Calen + 2% CHX and 2% CHX gel were compared, the latter induced more intense inflammatory response. Microsc. Res. Tech., 2012. (C) 2012 Wiley Periodicals, Inc.

Response of mice connective tissue to intracanal dressings containing chlorhexidine

Substances containing chlorhexidine (CHX) have been studied as intracanal medicaments. The aim of the present study was to characterize the response of mouse subcutaneous connective tissue to CHX-containing medications by conventional optical microscopy. The tissue response was evaluated by implanting polyethylene tubes containing one of the substances evaluated: Calen paste + 0.5% CHX, Calen + 2% CHX, 2% CHX gel, and Calen paste (control). After experimental periods of 7, 21, and 63 days, the implants (n = 10) were removed along with the subcutaneous connective tissue. Tissue samples were subjected to histological processing, and sections were stained with hematoxylin and eosin. Qualitative and quantitative analyses of the number of inflammatory cells, blood vessels, and vascularized areas were performed. Results were analyzed by ANOVA and Tukey tests with the significance level set at 5%. We concluded that Calen + 0.5% CHX led to reparative tissue response in contrast with Calen + 2% CHX and 2% CHX gel, which induced persistent inflammatory response, pointing to the aggressive nature of this mixture. When Calen + 2% CHX and 2% CHX gel were compared, the latter induced more intense inflammatory response. Microsc. Res. Tech., 2012. (C) 2012 Wiley Periodicals, Inc.

A new model construction by making a detour via intuitionistic theories I: Operational set theory without choice is Π1-equivalent to KP

We introduce a version of operational set theory, OST−, without a choice operation, which has a machinery for Δ0Δ0 separation based on truth functions and the separation operator, and a new kind of applicative set theory, so-called weak explicit set theory WEST, based on Gödel operations. We show that both the theories and Kripke–Platek set theory KPKP with infinity are pairwise Π1Π1 equivalent. We also show analogous assertions for subtheories with ∈-induction restricted in various ways and for supertheories extended by powerset, beta, limit and Mahlo operations. Whereas the upper bound is given by a refinement of inductive definition in KPKP, the lower bound is by a combination, in a specific way, of realisability, (intuitionistic) forcing and negative interpretations. Thus, despite interpretability between classical theories, we make “a detour via intuitionistic theories”. The combined interpretation, seen as a model construction in the sense of Visser's miniature model theory, is a new way of construction for classical theories and could be said the third kind of model construction ever used which is non-trivial on the logical connective level, after generic extension à la Cohen and Krivine's classical realisability model.