Käyttäjien manipulointi internetin yhteisöpalveluissa: Systemaattinen kirjallisuuskatsaus

Internetin yhteisöpalveluiden käyttäjien avoimuus ja sosiaalisuus altistavat heidät monenlaisille riskeille. “Social engineering” eli käyttäjien manipulointi on uhka, joka liittyy informaation hankkimiseen perinteisen kanssakäymisen kautta, mutta yhä enenevissä määrin myös internetissä. Kun kanssakäyminen tapahtuu internetin välityksellä, käyttäjien manipuloijat hyödyntävät yhteisöpalveluita yhteydenpitoon uhrien kanssa sekä paljon käyttäjäinformaatiota sisältävänä alustana. Tämän tutkielman tarkoitus on löytää internetin yhteisöpalveluiden ja käyttäjien manipuloinnin välinen yhteys. Tämä päämäärä saavutettiin etsimällä vastauksia kysymyksiin kuten: Mitkä ovat tyypilliset hyökkäystyypit? Miksi informaatiolla on niin suuri rooli? Mitä seurauksia ilmiöllä on ja miten hyökkäyksiltä on mahdollista suojautua? Vastaukset kysymyksiin löydettiin toteuttamalla systemaattinen kirjallisuuskatsaus. Katsaus muodostui yhdistämällä tärkeimmät löydökset 60 tarkoin valitusta ilmiötä käsittelevästä artikkelista. Käyttäjien manipuloinnin huomattiin olevan hyvin laaja ja monimutkainen ilmiö internetin yhteisöpalveluissa. Huomattiin, että manipulointia ilmenee sivustoilla useissa erilaisissa muodoissa, joita ovat muun muassa tietojen kalastelu, profiilien yhdistäminen, sosiaaliset sovellukset, roskaposti, haitalliset linkit, identiteettivarkaudet, tietovuodot ja erilaiset huijaukset, jotka hyödyntävät sekä ihmisluonnon että sivustojen perusominaisuuksia. Haavoittuvuus ja luottamus havaittiin myös tärkeiksi aspekteiksi, sillä ne yhdistävät informaation merkityksen ja ihmisluonnon, jotka molemmat ovat avaintekijöitä sekä manipuloinnissa että yhteisöpalvelusivustoilla. Vaikka ilmiön seurausten huomattiin olevan negatiivisia niin käyttäjien olemukselle internetissä kuin todellisessakin elämässä, havaittiin myös, että ilmiön ymmärtäminen ja tunnistaminen helpottaa siltä suojautumista

Caracterización de la cultura organizacional y lineamientos de intervención para la implementación de procesos de cambio en las organizaciones. Caso empresa sector financiero

El presente Trabajo de Grado busca caracterizar la cultura organizacional de una empresa del sector Financiero en Colombia y realizar orientaciones de acciones para el cambio organizacional de acuerdo con la estrategia de perdurabilidad establecida por la Alta Dirección de dicha empresa. Para este fin, se realiza una cuidadosa revisión y actualización del estado del arte de los conceptos clave ¨Cultura Organizacional¨ y ¨Cambio Organizacional¨. Es de resaltar que para el primero de ellos, se toma como punto de partida el estado del arte sobre Cultura Organizacional realizado por el profesor Carlos Eduardo Méndez Álvarez y cuyo marco temporal abarca desde los orígenes del concepto en el siglo XIX hasta el año 2006. Asimismo, luego de una cuidadosa revisión de los Modelos de Cambio Organizacional existentes y de la realidad de la empresa objeto de estudio, se adopta el Modelo ADKAR que consta de cinco fases: Conciencia del Cambio, Deseo, Conocimiento, Capacidad – Habilidad y Refuerzo. Asimismo, a partir de la construcción de un fundamento teórico sólido y a través de la aplicación de la metodología para describir la Cultura Organizacional en Colombia MEDECO se busca una aproximación a la Cultura Organizacional de la empresa objeto de estudio con el fin de describir e identificar los rasgos predominantes de su cultura organizacional y entregar una propuesta final con los rasgos necesarios que alientan la consecución exitosa de los procesos de cambio.

The potential for community groups to promote sustainable living

In recent times there has been a growing recognition amongst policy-makers of the role for community-based action in contributing to the broader aims of energy policy and climate change. In this paper, we will examine the potential for existing community groups to use their influence and elements of internal cohesion to encourage more widespread understanding and adoption of sustainable lifestyle habits; both amongst their members and within the broader communities of which they are a part. Findings are presented from recent empirical work with a range of well-established community groups for whom environmental issues are not their main priority. A central aspect of the research was to explore both the current status and potential role of groups that may have the capacity to reach and influence a broader sphere of the public than energy/environment specific initiatives of recent times have been able to achieve. Representing a diversity of interests, age groups and functionality, the results suggest that the potential for more effective ‘bottom-up’ engagement on climate change and sustainable living might be given fresh impetus by these types of established community groups and their networks. An assessment of what motivates participation and membership in the groups highlights a series of factors common to all groups and a smaller number that are significant for particular groups individually. It is argued that an appreciation of motivating factors can be useful in understanding more clearly how such groups are able to survive and maintain cohesion over time. The findings also suggest that climate change action means different things for different groups, with the diversity of the groups bringing with it the challenge of making sustainable living relevant to a range of interests and different shared values.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.

Infeasibility handling in genetic algorithm using nested domains for production planning

In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers. (C) 2009 Elsevier Ltd. All rights reserved.

Heuristics for the one-dimensional cutting stock problem with limited multiple stock lengths

This paper deals with the classical one-dimensional integer cutting stock problem, which consists of cutting a set of available stock lengths in order to produce smaller ordered items. This process is carried out in order to optimize a given objective function (e.g., minimizing waste). Our study deals with a case in which there are several stock lengths available in limited quantities. Moreover, we have focused on problems of low demand. Some heuristic methods are proposed in order to obtain an integer solution and compared with others. The heuristic methods are empirically analyzed by solving a set of randomly generated instances and a set of instances from the literature. Concerning the latter. most of the optimal solutions of these instances are known, therefore it was possible to compare the solutions. The proposed methods presented very small objective function value gaps. (C) 2008 Elsevier Ltd. All rights reserved.

Lot sizing and furnace scheduling in small foundries

A lot sizing and scheduling problem prevalent in small market-driven foundries is studied. There are two related decision levels: (I the furnace scheduling of metal alloy production, and (2) moulding machine planning which specifies the type and size of production lots. A mixed integer programming (MIP) formulation of the problem is proposed, but is impractical to solve in reasonable computing time for non-small instances. As a result, a faster relax-and-fix (RF) approach is developed that can also be used on a rolling horizon basis where only immediate-term schedules are implemented. As well as a MIP method to solve the basic RF approach, three variants of a local search method are also developed and tested using instances based on the literature. Finally, foundry-based tests with a real-order book resulted in a very substantial reduction of delivery delays and finished inventory, better use of capacity, and much faster schedule definition compared to the foundry`s own practice. (c) 2006 Elsevier Ltd. All rights reserved.

Entropy-Based Approach to Analyze and Classify Mineral Aggregates

This paper presents an automatic method to detect and classify weathered aggregates by assessing changes of colors and textures. The method allows the extraction of aggregate features from images and the automatic classification of them based on surface characteristics. The concept of entropy is used to extract features from digital images. An analysis of the use of this concept is presented and two classification approaches, based on neural networks architectures, are proposed. The classification performance of the proposed approaches is compared to the results obtained by other algorithms (commonly considered for classification purposes). The obtained results confirm that the presented method strongly supports the detection of weathered aggregates.

Multiscale Curvature Analysis of Asphaltic Aggregate Particles

A new method for characterization and analysis of asphaltic mixtures aggregate particles is reported. By relying on multiscale representation of the particles, curvature estimation, and discriminant analysis for optimal separation of the categories of mixtures, a particularly effective and comprehensive methodology is obtained. The potential of the methodology is illustrated with respect to three important types of particles used in asphaltic mixtures, namely basalt, gabbro, and gravel. The obtained results show that gravel particles are markedly distinct from the other two types of particles, with the gabbro category resulting with intermediate geometrical properties. The importance of each considered measurement in the discrimination between the three categories of particles was also quantified in terms of the adopted discriminant analysis.

Orthogonal packing of identical rectangles within isotropic convex regions

A mixed integer continuous nonlinear model and a solution method for the problem of orthogonally packing identical rectangles within an arbitrary convex region are introduced in the present work. The convex region is assumed to be made of an isotropic material in such a way that arbitrary rotations of the items, preserving the orthogonality constraint, are allowed. The solution method is based on a combination of branch and bound and active-set strategies for bound-constrained minimization of smooth functions. Numerical results show the reliability of the presented approach. (C) 2010 Elsevier Ltd. All rights reserved.

New and improved results for packing identical unitary radius circles within triangles, rectangles and strips

The focus of study in this paper is the class of packing problems. More specifically, it deals with the placement of a set of N circular items of unitary radius inside an object with the aim of minimizing its dimensions. Differently shaped containers are considered, namely circles, squares, rectangles, strips and triangles. By means of the resolution of non-linear equations systems through the Newton-Raphson method, the herein presented algorithm succeeds in improving the accuracy of previous results attained by continuous optimization approaches up to numerical machine precision. The computer implementation and the data sets are available at http://www.ime.usp.br/similar to egbirgin/packing/. (C) 2009 Elsevier Ltd, All rights reserved.

Three-dimensional packings with rotations

We present approximation algorithms for the three-dimensional strip packing problem, and the three-dimensional bin packing problem. We consider orthogonal packings where 90 degrees rotations are allowed. The algorithms we show for these problems have asymptotic performance bounds 2.64, and 4.89, respectively. These algorithms are for the more general case in which the bounded dimensions of the bin given in the input are not necessarily equal (that is, we consider bins for which the length. the width and the height are not necessarily equal). Moreover, we show that these problems-in the general version-are as hard to approximate as the corresponding oriented version. (C) 2009 Elsevier Ltd. All rights reserved.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the Most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions. (C) 2008 Elsevier B.V. All rights reserved.

Minimizing the object dimensions in circle and sphere packing problems

Given a fixed set of identical or different-sized circular items, the problem we deal with consists on finding the smallest object within which the items can be packed. Circular, triangular, squared, rectangular and also strip objects are considered. Moreover, 2D and 3D problems are treated. Twice-differentiable models for all these problems are presented. A strategy to reduce the complexity of evaluating the models is employed and, as a consequence, instances with a large number of items can be considered. Numerical experiments show the flexibility and reliability of the new unified approach. (C) 2007 Elsevier Ltd. All rights reserved.