31100803
Web Search Based on Ant Behavior: Approach and Implementation in Case of Interlegis
The World Wide Web today is considered the biggest information and knowledge source in every field. These Web pages are accessed daily by a great quantity of people with different interests. However, they are heterogeneously constructed both in available information and quality and majorities are presented in HTML language. This manner limits and makes it difficult to extract useful information and knowledge using simple search by Internet. The process of surfing the Web is similar to ant colonies foraging (Beckers, Deneubourg & AbstrAct
42414770
Distributed Knowledge Based System Using Grid Computing for Real Time Air Traffic Synchronization - ATFMGC
12034739
An Algorithm for Determining the Controllers of Supervised Entities at the First and Second Levels: A Case Study with the Brazilian Central Bank
An algorithm was developed and implemented to find controllers from the stock shares for some financial institutions for the Brazilian Central Bank (BCB). The original problem is similar to a typical Sum of Subset problem that is suggested to be solved by a backtracking algorithm and the problem complexity is NP-complete. Usually BCB solves this problem manually which is time consuming and prone to errors. The heuristical approximation algorithm presented in this paper has polynomial complexity O(n3) and is based on subroutines for determining controllers at the first two levels. The paper describes the basic concepts and business rules currently employed in BCB, our algorithm and its major subroutines, it gives a brief complexity analysis and an example illustration at level 2. Our experimental results indicate the feasibility of an automation of the process of finding controllers. Though developed for BCB, our algorithm works equally well for other financial institutions.
37514455
An Algorithm for Determining the Controllers of Supervised Entities at the First Level: Brazilian Central Bank Case Study
To find controllers from the stock shares of a financial institution is an important task in the Brazilian Central Bank (BCB). The original problem is typically called Sum of Subset and suggested to be resolved by backtrack algorithm with NP-complete complexity. Currently BCB solves this problem manually which is time consuming and suffered from errors. In this study, an approximation algorithm with polynomial complexity O(n) for determining controllers was developed and implemented in BCB. This algorithm includes subroutines for determining controller at levels 1, 2, 3 and the main loops of combination. This paper describes the basic concepts and business rules currently employed in BCB, complexity analysis of the algorithm and its step by step applications at the first level. The implementation and experiment results indicate the feasibility of automation of controllers finding process. Though developed for BCB, it works equally well for all financial institutions which are controlled by stockholders in Brazil.
26434237
Method to Balance the Communication Among Multi-agents in Real Time Traffic Synchronization
A method to balance the communication among Multi-Agents in real time traffic synchronization is proposed in this research. The paper presents Air Traffic Flow Management (ATFM) problem and its synchronization property. For such a complex problem, combing grid computing with multi-agent coordination techniques to improve ATFM computational efficiency is the main objective of actual research. To demonstrate the developed model – ATFM in Grid Computing (ATFMGC), the grid architecture, the basic components and the relationship among them are described. At the same time, the function of agents (tactical planning agent etc.), their knowledge representation and inference processes are also discussed. As criteria to measure the effective to reduce quantity of the communication among agents and the delay of the flights, Standard of Balancing among Agents (SBA) is used in the analysis. The simulation shows the efficiency of the developed model and successful application in the case study.
18404050
AntWeb - the adaptive Web server based on the ants' behavior
We present the AntWeb system, developed under the research area of Web Intelligence (WI). Our approach to AntWeb application is inspired by the ant colonies foraging behavior, to adaptively mark the most significant links, by means of the shortest route to arrive to target pages. We consider the Web users as artificial ants, and use the ant theory as a metaphor to guide user's activity in the Web site. We describe the ant's theory in which AntWeb is based on. We also present the AntWeb system, its implementation and a case study with some experiments. The database in AntWeb stores a vast amount of information related to the users' visit to Web sites, which can be useful for further Web mining.
16400135
Adaptive grid-based document layout
Grid-based page designs are ubiquitous in commercially printed publications, such as newspapers and magazines. Yet, to date, no one has invented a good way to easily and automatically adapt such designs to arbitrarily-sized electronic displays. The difficulty of generalizing grid-based designs explains the generally inferior nature of on-screen layouts when compared to their printed counterparts, and is arguably one of the greatest remaining impediments to creating on-line reading experiences that rival those of ink on paper. In this work, we present a new approach to adaptive grid-based document layout, which attempts to bridge this gap. In our approach, an adaptive layout style is encoded as a set of grid-based templates that know how to adapt to a range of page sizes and other viewing conditions. These templates include various types of layout elements (such as text, figures, etc.) and define, through constraint-based relationships, just how these elements are to be laid out together as a function of both the properties of the content itself, such as a figure's size and aspect ratio, and the properties of the viewing conditions under which the content is being displayed. We describe an XML-based representation for our templates and content, which maintains a clean separation between the two. We also describe the various parts of our research prototype system: a layout engine for formatting the page; a paginator for determining a globally optimal allocation of content amongst the pages, as well as an optimal pairing of templates with content; and a graphical user interface for interactively creating adaptive templates. We also provide numerous examples demonstrating the capabilities of this prototype, including this paper, itself, which has been laid out with our system.
6624035
Weakly Supervised Learning for Cross-document Person Name Disambiguation Supported by Information Extraction
It is fairly common that different people are associated with the same name. In tracking person entities in a large document pool, it is important to determine whether multiple mentions of the same name across documents refer to the same entity or not. Previous approach to this problem involves measuring context similarity only based on co-occurring words. This paper presents a new algorithm using information extraction support in addition to co-occurring words. A learning scheme with minimal supervision is developed within the Bayesian framework. Maximum entropy modeling is then used to represent the probability distribution of context similarities based on heterogeneous features. Statistical annealing is applied to derive the final entity coreference chains by globally fitting the pairwise context similarities. Benchmarking shows that our new approach significantly outperforms the existing algorithm by 25 percentage points in overall F-measure.
11433773
Email archive analysis through graphical visualization
The analysis of the vast storehouse of email content accumulated or produced by individual users has received relatively little attention other than for specific tasks such as spam and virus filtering. Current email analysis in standard client applications consists of keyword based matching techniques for filtering and expert driven manual exploration of email files. 
 We have implemented a tool, called the Email Mining Toolkit (EMT) for analyzing email archives which includes a graphical display to explore relationships between users and groups of email users. The chronological flow of an email message can be analyzed by EMT. Our design goal is to embed the technology into standard email clients, such as Outlook, revealing far more information about a user's own email history than is otherwise now possible. In this paper we detail the visualization techniques implemented in EMT. We show the utility of these tools and underlying models for detecting email misuse such as viral propagation, and spam spread as examples.
6649365
Performing Incremental Bayesian Inference by Dynamic Model Counting
The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 2005) demonstrates that the well-known Davis-Putnam procedure combined with a dynamic decomposition and caching technique is an effective method for exact inference in Bayesian networks with high density and width. In this paper, we define dynamic model counting and extend the dynamic decomposition and caching technique to multiple runs on a series of problems with similar structure. This allows us to perform Bayesian inference incrementally as the structure of the network changes. Experimental results show that our approach yields significant improvements over the previous model counting approaches on multiple challenging Bayesian network instances.
10927916
Integration of Image Matching and Classification for Multimedia Navigation
With the recent explosive growth in the volume of images on the World-Wide Web, it has become increasingly difficult to search for images of interests. The classification of images helps users to access a large image collection efficiently. Classification reduces search space by filtering out unrelated images. Classification also allows for more user-friendly interfaces: users can better visualize easily result space by browsing the representative images of the candidates. In this paper, we present a technique for image classification based on color, shape and composition using the primary objects. We apply this classification technique in image matching for image retrieval on the Web. Our experimental results show that this approach can maintain 73% of recall by searching only 24% of the whole data set. We also show how we apply such technique to assist users in navigation.