Moral perfectionism and moral values, virtues, and judgments: A preliminary investigation

Moral perfectionism has a long tradition in philosophical inquiry, but so far has been ignored in psychological research. This article presents a first psychological investigation of moral perfectionism exploring its relationships with moral values, virtues, and judgments. In three studies, 539 university students responded to items of the Frost Multidimensional Perfectionism Scale (Frost, Marten, Lahart, & Rosenblate, 1990) adapted to measure personal moral standards (PMS) and concern over moral mistakes (CMM) and completed measures of moral values, virtues, and forgiveness, gratitude, and wrong behavior judgments. When partial correlations were computed controlling for the overlap between PMS and CMM, PMS showed positive correlations with moral values, virtues, reciprocal helping, forgiveness, and condemnation of wrong behaviors. In contrast, CMM showed a positive correlation only with indebtedness and a negative correlation with self-reliance. The present findings, while preliminary, suggest that moral perfectionism is a personality characteristic that may help explain individual differences in moral values, virtues, and judgments

Location-based indexing for mobile context-aware access to a digital library

Mobile information systems need to collaborate with each other to provide seamless information access to the user. Information about the user and their context provides the points of contact between the systems. Location is the most basic user context. TIP is a mobile tourist information system that provides location-based access to documents in the digital library Greenstone. This paper identifies the challenges for providing effcient access to location-based information using the various access modes a tourist requires on their travels. We discuss our extended 2DR-tree approach to meet these challenges

Building nearest prototype classifiers using a Michigan approach PSO

IEEE Swarm Intelligence Symposium. Honolulu, HI, 1-5 april 2007This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results

The Skip Quadtree: A Simple Dynamic Data Structure for Multidimensional Data

We present a new multi-dimensional data structure, which we call the skip quadtree (for point data in R^2) or the skip octree (for point data in R^d, with constant d>2). Our data structure combines the best features of two well-known data structures, in that it has the well-defined "box"-shaped regions of region quadtrees and the logarithmic-height search and update hierarchical structure of skip lists. Indeed, the bottom level of our structure is exactly a region quadtree (or octree for higher dimensional data). We describe efficient algorithms for inserting and deleting points in a skip quadtree, as well as fast methods for performing point location and approximate range queries.Comment: 12 pages, 3 figures. A preliminary version of this paper appeared in the 21st ACM Symp. Comp. Geom., Pisa, 2005, pp. 296-30

Modern public finances as a proposal for an emerging country: The social approach in the fight against poverty in Mexico

In Mexico, the management of public resources has been questioned by the State, and mainly the results that the public administration at its three levels (federal, state and municipal), by the lack of transparency in the application and verification of public resources. The experience that gives us the operation of different emerging programs that focused on reducing social and economic inequality in the country, we can locate them as the first attempts in the search for a solution that is complex. Moving from the role of the benefactor and welfare state to the promoter of the regions and in the recognition of the focalization of priority attention areas, the path that has been taken is not only the beginning. Recognizing the public nature of public finances as a promoter of social development, we must understand it as the one assumed by the State through social, political and economic co-responsibility to solve poverty and marginalization of its own public policy orientation and vision of solution has been made since the eighties. From the above, we can point out some preliminary conclusions including the study of the indigenous language-speaking population with a high level of social exclusion in the methodology for the definition of multidimensional poverty in Mexico, will allow the allocation of public resources in the fight against poverty to be effective since it will make it possible to identify to the target population that is subject to social exclusion and marginalization. This invites us to a final reflection: What to do to address the just social demands of the indigenous population that is immersed in poverty, marginalization and exclusion? What to do so that they do not leave their communities, and if they have already done so, how to attend to the needs of family groups that are due to the expense of a remittance that may never arrive

A Weight-coded Evolutionary Algorithm for the Multidimensional Knapsack Problem

A revised weight-coded evolutionary algorithm (RWCEA) is proposed for solving multidimensional knapsack problems. This RWCEA uses a new decoding method and incorporates a heuristic method in initialization. Computational results show that the RWCEA performs better than a weight-coded evolutionary algorithm proposed by Raidl (1999) and to some existing benchmarks, it can yield better results than the ones reported in the OR-library.Comment: Submitted to Applied Mathematics and Computation on April 8, 201

Alternation-Trading Proofs, Linear Programming, and Lower Bounds

A fertile area of recent research has demonstrated concrete polynomial time lower bounds for solving natural hard problems on restricted computational models. Among these problems are Satisfiability, Vertex Cover, Hamilton Path, Mod6-SAT, Majority-of-Majority-SAT, and Tautologies, to name a few. The proofs of these lower bounds follow a certain proof-by-contradiction strategy that we call alternation-trading. An important open problem is to determine how powerful such proofs can possibly be. We propose a methodology for studying these proofs that makes them amenable to both formal analysis and automated theorem proving. We prove that the search for better lower bounds can often be turned into a problem of solving a large series of linear programming instances. Implementing a small-scale theorem prover based on this result, we extract new human-readable time lower bounds for several problems. This framework can also be used to prove concrete limitations on the current techniques.Comment: To appear in STACS 2010, 12 page

0-1 Integer Linear Programming with a Linear Number of Constraints

We give an exact algorithm for the 0-1 Integer Linear Programming problem with a linear number of constraints that improves over exhaustive search by an exponential factor. Specifically, our algorithm runs in time $2^{(1-\text{poly}(1/c))n}$ where n is the number of variables and cn is the number of constraints. The key idea for the algorithm is a reduction to the Vector Domination problem and a new algorithm for that subproblem