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.
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.
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.
36399349
Optimal (N, T)-policy for M/G/1 system with cost structures
Abstract In this paper, the optimal (N,T)-policy for M/G/1 system with cost structure is studied. The system operates only intermittently. It is shut down when no customers are present. A fixed set-up cost of K>0 is incurred each time the system is reopened. Also, a holding cost of h>0 per unit time is incurred for each customer present. The (N,T)-policy studied for this system is as follows: the system reactivates as soon as N customers are present or the waiting time of the leading customer reaches a predefined time T (see A.S. Alfa, I. Frigui, Eur. J. Oper. Res. 88 (1996) 599-613; Y.N. Doganata, in: E. Arikan (Ed.), Communication, Control, and Signal Processing, 1990, pp. 1663–1669). Later on, as a comparison, the start of the timer count is relaxed as follows: the system reactivates as soon as N customers are present or the time units after the end of the last busy period reaches a predefined time T. For both cases, the explicit optimal policy (N*,T*) for minimizing the long-run average cost per unit time are obtained. As extreme cases, we include the simple optimal policies for N-and T-polices. Several counter-intuitive results are obtained about the optimal T-policies for both types of models.
12496830
A Fast Algorithm for Color Image Segmentation
Based on K-means and a two-layer pyramid structure, a fast algorithm is proposed for color image segmentation. The algorithm employs two strategies. Firstly, a two-layer structure of a color image is established. Then, an improved K-means with integer based lookup table implementation is applied to each layer. The clustering result on the upper layer (lower resolution) is used to guide the clustering in the lower layer (higher resolution). Experiments have shown that the proposed algorithm is significantly faster than the original K-means algorithm while producing comparable segmentation results
8035776
Semantic Integration in Heterogeneous Databases Using Neural Networks
One important step in integrating heterogeneous databases is matching equivalent attributes: Determining which fields in two databases refer to the same data. The meaning of information may be embodied within a. database model, a conceptual schema, application programs, or data contents. Integration involves extracting semantics, expressing them as metadata, and matching semantically equivalent data elements. We present a procedure using a classifier to categorize attributes according to their field specifications and data values, then train a neural network to recognize similar attributes. In our technique, the knowledge of how to match equivalent data elements is “discovered” from metadata , not “pre-programmed”.
9473484
Bootstrapping for Named Entity Tagging Using Concept-based Seeds
A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. The bootstrapping procedure is implemented as training two successive learners. First, decision list is used to learn the parsing-based NE rules. Then, a Hidden Markov Model is trained on a corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types.