Finding a good figure that approximately passes through given points

A system is presented which, when given a list of points on the plane, will find a good figure to approximately pass through the points. Some of the thirteen figures considered are the line segment, circle, parallelogram, and equilateral triangle. The system searches a disjunctive (or) goal tree. We are performing research needed in the development of a robot manipulator system. Object recognition is a very important part of the system. Although the robot will be sensing three-dimensional objects from tactile or other sensors, there are reasons for first treating the two-dimensional case.     

A soft computing model for mapping incomplete/approximate postal addresses to mail delivery points

Mapping of postal address to a mail delivery point is a very important task that affects the efficiency of postal service. This task is very complex in the countries such as India, where postal addresses are not structured. Further most of the times the destination addresses in such countries are incomplete, approximate and erroneous which adds to the complexity of mapping postal address to delivery point. Automation of this aspect of the postal service is a challenge. This paper presents a soft computing model to map the postal address to mail delivery point. Firstly machine readable postal address is processed to identify the address components using a novel fuzzy symbolic similarity analysis, and further these labeled components are organized as a symbolic postal address object. This postal address object is further processed using the newly devised fuzzy symbolic methodology for mapping the address to mail delivery point. Symbolic knowledge bases for postal address component labeling and mail delivery point mapping are devised. Fuzzy symbolic similarity measures are formulated once for address component labeling and the second time for mapping the entire address to a mail delivery point. In sequel to similarity computations, which are viewed as fuzzy membership values, an expert system comprising of α-cut de-fuzzification is proposed to evaluate the confidence factors, while inferencing the validity of address component labels and mail delivery points. The system is tested exhaustively and an efficiency of 94% is obtained in address component identification and about 86% in mail delivery point mapping, while working on an Indian postal data base of about 500 addresses.     

On an approximate minimax circle closest to a set of points

We show how the Chebychev minimax criterion for finding a circle closest to a set of points can be approximated well by standard linear programming procedures.Scope and purposeProblems that arise in location theory and in the quality control of manufactured parts (drilled holes, shaped spheres) call for finding an annulus of minimum width that encompasses a set of points. For the two-dimensional case, this is equivalent to determining a closest “deviation” circle with center (x₀,y₀) and radius r₀ such that the maximum radial distance of the points to the circumference of the deviation circle is minimized. The required annulus (narrowest ring) is formed by two circles, centered at (x₀,y₀), that inscribe and circumscribe the given set of points. We suggest that our linear-programming procedure be used to approximate this annulus as, unlike exact methods, it is stable, fast, and generalizes readily to higher-dimensional point sets.     

A linear time deterministic algorithm to find a small subset that approximates the centroid

Given a set of points S in any dimension, we describe a deterministic algorithm for finding a such that the centroid of T approximates the centroid of S within a factor 1+ε for any fixed ε>0. We achieve this in linear time by an efficient derandomization of the algorithm in [M. Inaba, N. Katoh, H. Imai, Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering (extended abstract), in: Proceedings of the Tenth Annual Symposium on Computational Geometry, 1994, pp. 332-339].     

A new method to find neighbor users that improves the performance of Collaborative Filtering

Recommender Systems (RS) are used to help people reduce the amount of time they spend to find the items they are looking for. One of the most successful techniques used in RS is called Collaborative Filtering (CF). It looks into the choices made by other users to find items that are most similar to the target user. Data sparsity and high dimensionality which are common in the RS domains have negatively affected the efficiency of CF. The current paper seeks to solve the mentioned problems through a neighbor user finding method which has been derived from the subspace clustering approach. In this method, the authors extract different subspaces of rated items under the categories of Interested, Neither Interested Nor Uninterested, and Uninterested. Based on subspaces, tree structures of neighbor users are drawn for the target user. Furthermore, a new similarity method is proposed to compute the similarity value. This new method has been tested via the Movielens 100K, Movielens 1M and Jester datasets in order to make a comparison with the traditional techniques. The results have indicated that the proposed method can enhance the performance of the Recommender Systems.     

A piecewise linear approximation method preserving visual feature points of original figures

We present a method for piecewise linear approximation of digitized planar curves which can compress the amount of data, without loss of perceptual information for human approximation. This method consists of two stages, the uniform process and the recovering process. The method has the following four major merits: (1) Because this method approximates according to the intricacy, it can efficiently approximate figures in which portions of both large and small curvatures coexist. (2) Spikes and corners can be retained. (3) Smooth connecting points are retained. (4) Straight lines in the original figure are directly adopted as approximating line segments. Because of the above merits, our method is applicable to both natural and artificial figures.     

A numerical method to find optimal submeasures

Algorithms are developed that find the optimum of a functional dependent on probability measures subject to special constraints.Translated from Kibernetika, No. 2, pp. 70–77, March–April, 1990.     

爱敏捷，爱自由

仲夏之夜,一个五层楼的天台上传出轻快的音乐,一群年轻人聚集在这里,桌子上摆放着各种颜色的酒水和饮料,中间的两个大横桌上摆放了各种精致的美食。一阵欢快的笑声传来,寻声望去,天台的一角有一个桌上足球案,几个年轻人正在激烈的对逐。而天台的另一侧则摆放着两个红彤彤的炭火炉。     

PumpKin: A tool to find principal pathways in plasma chemical models

PumpKin is a software package to find all principal pathways, i.e. the dominant reaction sequences, in chemical reaction systems. Although many tools are available to integrate numerically arbitrarily complex chemical reaction systems, few tools exist in order to analyze the results and interpret them in relatively simple terms. In particular, due to the large disparity in the lifetimes of the interacting components, it is often useful to group reactions into pathways that recycle the fastest species. This allows a researcher to focus on the slow chemical dynamics, eliminating the shortest timescales. Based on the algorithm described by Lehmann (2004), PumpKin automates the process of finding such pathways, allowing the user to analyze complex kinetics and to understand the consumption and production of a certain species of interest. We designed PumpKin with an emphasis on plasma chemical systems but it can also be applied to atmospheric modeling and to industrial applications such as plasma medicine and plasma-assisted combustion.     

MCF: A tool to find multi-scale community profiles in biological networks

Recent developments of complex graph clustering methods have implicated the practical applications with biological networks in different settings. Multi-scale Community Finder (MCF) is a tool to profile network communities (i.e., clusters of nodes) with the control of community sizes. The controlling parameter is referred to as the scale of the network community profile. MCF is able to find communities in all major types of networks including directed, signed, bipartite, and multi-slice networks. The fast computation promotes the practicability of the tool for large-scaled analysis (e.g., protein–protein interaction and gene co-expression networks). MCF is distributed as an open-source C++ package for academic use with both command line and user interface options, and can be downloaded at http://bsdxd.cpsc.ucalgary.ca/MCF. Detailed user manual and sample data sets are also available at the project website.     

Managing network security: Why it was done that way

Computing operates in an almost universally networked environment, but the technical aspects of information protection have not kept up. As a result, the success of information security programs has increasingly become a function of our ability to make prudent management decisions about organizational activities. Managing Network Security takes a management view of protection and seeks to reconcile the need for security with the limitations of technology.     

Smoothing: A natural way to detect contour features

This work presents a dominant point detector. The angles of the contour are characterized through local entropy produced by a rotationally symmetric smoothing. The proposed scheme uses a punctual multi-scale approach in which only the candidates are analyzed in higher scales. To preserve the angle-entropy relationship in higher scales, we propose a smoothing kernel which presents special features that ensure its steepness in every scale. It is built from the sum of two Gaussians with different openings resembling center-surround receptive fields. The outputs of the proposed method are confronted to a ground-truth found in the literature, and to popular boundary based corner detectors that used the same set of images. Results reveal that the proposed detector performs extremely well.     

Web caching: A way to improve web QoS

As the Internet and World Wide Web grow at a fast pace, it is essential that the Web‘s performance should keep up with increased demand and expectations. Web Caching technology has.been widely accepted as one of the effective approaches to alleviating Web traffic and increase the Web Quality of Service (QoS). This paper provides an up-to-date survey of the rapidly expanding Web Caching literature. It discusses the state-of-the-art web caching schemes and techniques, with emphasis on the recent developments in Web Caching technology such as the differentiated Web services, heterogeneous caching network structures, and dynamic content caching.     

Confronting tipping points: Can multi-objective evolutionary algorithms discover pollution control tradeoffs given environmental thresholds?

This study contributes a stochastic, multi-objective adaptation of the classic environmental economics Lake Problem as a computationally simple but mathematically challenging benchmarking problem. The Lake Problem considers a hypothetical town by a lake, which hopes to maximize its economic benefit without crossing a nonlinear, and potentially irreversible, pollution threshold. Optimization objectives are maximize economic benefit, minimize phosphorus in the lake, maximize the probability of avoiding the pollution threshold, and minimize the probability of drastic phosphorus loading reductions in a given year. Uncertainty is introduced through a stochastic natural phosphorus inflow. We performed comprehensive diagnostics using six algorithms: the Borg multi-objective evolutionary algorithm (MOEA), MOEA/D, epsilon-MOEA, the Non-dominated Sorting Genetic Algorithm II (NSGAII), epsilon-NSGAII, and Generalized Differential Evolution 3 (GDE3) to evaluate their controllability, reliability, efficiency, and effectiveness. Our results show only the self-adaptive search of the Borg MOEA was capable of performing well on this nontrivial benchmarking problem.     

A complete algorithm to find exact minimal polynomial by approximations

Based on an improved parameterized integer relation construction method, a complete algorithm is proposed for finding an exact minimal polynomial from its approximate root. It relies on a study of the error controlling for its approximation. We provide a sufficient condition on the precision of the approximation, depending only on the degree and the height of its minimal polynomial. Our result is superior to the existent error controlling on obtaining an exact rational or algebraic number from its approximation. Moreover, some applications are presented and compared with the subsistent methods.