On generalized Bonferroni mean operators for multi-criteria aggregation

In embodiments of the present invention improved capabilities are described for multiple web-based content category searching for web content on a mobile communication facility comprising capturing speech presented by a user using a resident capture facility on the mobile communication facility; transmitting at least a portion of the captured speech as data through a wireless communication facility to a speech recognition facility; generating speech-to-text results for the captured speech utilizing the speech recognition facility; and transmitting the text results and a plurality of formatting rules specifying how search text may be used to form a query for a search capability on the mobile communications facility, wherein each formatting rule is associated with a category of content to be searched.

Multiple web-based content category searching in mobile search application

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.

/pdf/hierarchy-measure-for-complex-networks-1cjlmppl3t.pdf

Hierarchy measure for complex networks.

In embodiments of the present invention improved capabilities are described for interacting with a mobile communication facility comprising receiving a switch activation from a user to initiate a speech recognition recording session, wherein the speech recognition recording session comprises a voice command from the user followed by the speech to be recognized from the user; recording the speech recognition recording session using a mobile communication facility resident capture facility; recognizing at least a portion of the voice command as an indication that user speech for recognition will begin following the end of the at least a portion of the voice command; recognizing the recorded speech using a speech recognition facility to produce an external output; and using the selected output to perform a function on the mobile communication facility.

Dealing with switch latency in speech recognition

http://dro.deakin.edu.au/eserv/DU:30031468/beliakov-generalized-post-2010.pdf

Generalized Bonferroni mean operators in multi-criteria aggregation

Generalized conjunction/disjunction

This paper provides a novel algorithm to automatically detect the hidden hierarchy in terrorist networks. The algorithm is based on centrality measures used in social network analysis literature. The advantage of such automatic methods is to detect key players in terrorist networks. We illustrate the algorithm over some case studies of terrorist events that have occurred in the past. The results show great promise in detecting high value individuals.

Detecting Hidden Hierarchy in Terrorist Networks: Some Case Studies

We present an approach to a Genetic Information Retrieval Agent Filter (GIRAF) for documents from the Internet using a genetic algorithm (GA) with fuzzy set genes to learn the user’s information needs. The population of chromosomes with fixed length represents such user’s preferences. Each chromosome is associated with a fitness that may be considered the system’s belief in the hypothesis that the chromosome, as a query, represents the user’s information needs. In a chromosome, every gene characterizes documents by a keyword and an associated occurrence frequency, represented by a certain type of a fuzzy subset of the set of positive integers. Based on the user’s evaluation of the documents retrieved by the chromosome, compared to the scores computed by the system, the fitness of the chromosomes is adjusted. A prototype of GIRAF has been developed and tested. The results of the test are discussed, and some directions for further works are pointed out.

A fuzzy genetic algorithm approach to an adaptive information retrieval agent

Traditionally most of the literature in social network analysis (SNA) has focused on networks of individuals. Although SNA is not conventionally considered as a data mining technique, it is especially suitable for mining a large volume of association data to discover hidden structural patterns in terrorist networks. After September 11 attacks, SNA has increasingly been used to study terrorist networks. As these covert networks share some features with conventional networks, they are harder to identify because they mask their transactions. The most complicating factor is that terrorist networks are often embedded in a much larger population (i.e., adversaries have links with both covert and innocent individuals). Hence, it is desirable to have tools to correctly classify individuals in covert networks so that the resources for isolating them will be used more efficiently. This paper uses centrality measures from complex networks to discuss how to destabilize adversary networks. We propose newly introduced algorithms for constructing hierarchy of the covert networks, so that investigators can view the structure of the ad hoc networks/atypical organizations, in order to destabilize the adversaries. The algorithms are also demonstrated by using publicly available dataset. Moreover we also demonstrate techniques for filtering graphs (networks)/detecting particular cells in adversary networks using a fictitious dataset.

Notice of Violation of IEEE Publication Principles Practical approaches for analysis, visualization and destabilizing terrorist networks

The family of ordered weighted averaging (OWA) operators, as introduced by R.R. Yager (1988), appears to be very useful in flexible query answering, including information retrieval, where the information needs are often modeled by an aggregation (of the criteria in the query) between /spl and/ (pure AND) and V (pure OR). In this paper, we discuss and set up a general set of requirements for importance-weighted aggregation, and show that an importance weighting scheme suggested by Yager (1977) for OWA operators satisfies the requirements. The weighted arithmetic mean is shown to be order-equivalent to the special case of importance-weighted OWA operators where the importance-weighted satisfaction of the criteria are weighted evenly in the OWA aggregation.

Henrik Legind Larsen

Papers

Generalized conjunction/disjunction

Detecting Hidden Hierarchy in Terrorist Networks: Some Case Studies

A fuzzy genetic algorithm approach to an adaptive information retrieval agent

Notice of Violation of IEEE Publication Principles Practical approaches for analysis, visualization and destabilizing terrorist networks

Importance weighted OWA aggregation of multicriteria queries