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The purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration.

The authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage.

Clustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be.

The methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions.

This research provides evidence toward new recommendation methods directed toward LO repositories.

Force/torque sensing is very important for several automatic and industrial robotic applications. Basically, if precise control of the forces that arise from contact between tools and parts is required to successfully complete the automatic task, then a force/torque sensor is needed along with some force/torque control technique. In this paper we focus on force/torque sensing aspects applied to industrial robotic tasks. Concentrating on a particular type of force/torque sensor, we demonstrate how to use them and how to integrate them into force/torque control applications using robots. Finally, an industrial application is presented where force control was fundamental for the success of the task.

The aim of this paper is to perform an evaluation of the Brazilian informal labor market, following the lines suggested by Araujo and Souza. The paper focuses on the Brazilian labor market by using data from the National Household Sampling Survey – PNAD/IBGE, from 1995 to 2008.

The paper uses four different methodologies: the OLS, pseudo‐panel with fixed effects, instrumental variables and the Heckman selection model. The data for this study were obtained at the National Household Sampling Survey – PNAD/IBGE, from 1995 to 2008, with an exception to the years of 1994 and 2000, when this survey did not occur. The cross‐section data were pooled and divided in two periods: 1995‐2002 and 2003‐2008. The sample ranges individuals from 22 to 57 years of age.

The empirical findings show that although wages play an import role in the decision of workers and firms, all wage gaps between formal and informal markets reduced considerably, as has been pointed out by Mattos and Ogura. However, an increase in one year of study would raise the probability of participating in the formal sector while diminishing the probability of joining the informal sector. Besides, the skill premium in the formal sector is higher than in the informal sector.

Results indicate that the main difference between the 1995‐2002 and 2003‐2008 period is the impact of education over wages. Returns to education for the second period are greater than for the first period analyzed. The monetary gains for the first period vary from 16.5 to 18.2 percent for formality and from 12.2 to 14.3 percent for informal labor. Whereas, for the second period these gains range from 19.7 to 22.6 percent and from 16.9 to 18.2 percent, for formality and informality, respectively. According to these findings, investments made on education were more profitable for the 2003‐2008 period. However, all wage gaps between formal and informal markets reduced considerably, which reinforces the fact that workers are increasingly deciding in which market to operate.

The purpose of this paper is to present a new quantum‐inspired evolutionary hybrid intelligent (QIEHI) approach, in order to overcome the random walk dilemma for stock market prediction.

The proposed QIEHI method is inspired by the Takens' theorem and performs a quantum‐inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The approach presented in this paper consists of a quantum‐inspired intelligent model composed of an artificial neural network (ANN) with a modified quantum‐inspired evolutionary algorithm (MQIEA), which is able to evolve the complete ANN architecture and parameters (pruning process), the ANN training algorithm (used to further improve the ANN parameters supplied by the MQIEA), and the most suitable time lags, to better describe the time series phenomenon.

This paper finds that, initially, the proposed QIEHI method chooses the better prediction model, then it performs a behavioral statistical test to adjust time phase distortions that appear in financial time series. Also, an experimental analysis is conducted with the proposed approach using six real‐word stock market times series, and the obtained results are discussed and compared, according to a group of relevant performance metrics, to results found with multilayer perceptron networks and the previously introduced time‐delay added evolutionary forecasting method.

The paper usefully demonstrates how the proposed QIEHI method chooses the best prediction model for the times series representation and performs a behavioral statistical test to adjust time phase distortions that frequently appear in financial time series.

The purpose of this paper is to study the impact of terrorist actions and money laundering on capital accumulation and consumption.

The paper builds a general equilibrium model that considers the existence of two representative agents: a terrorist and a worker. It departs from a formalization of Linn's findings about the connection between money laundering and terrorism. His results are confirmed and extended.

The paper shows that the combat to money laundering is an effective way of preventing terrorist actions. Besides it is found that these actions do not affect the optimal stock of capital goods but have a negative impact on per capita consumption.

The paper confirms Linn's findings concerning money laundering and terrorism by using a formal approach. It also extends the model in order to study the impact of money laundering and terrorism on capital accumulation and consumption.

The purpose of this paper is to show that an evolutionary approach to combat money laundering can shed new lights on this matter.

An evolutionary game between financial institutions and employees is assumed in which the decisions of the banks and employees to cope against money laundering endogenously is evaluated. The players are allowed to review their strategies in each period of time comparing their payoffs with the average payoff.

The paper shows that the efficiency of anti‐money laundering combat relies on the conjugation of factors such as a proper design of the anti‐money laundering regulation and an endogenous willingness of banks and workers to cope against this war. On one hand, that the number of banks willing to fight money laundering affects the number of employees who also fight against money laundering. On the other hand, the number of banks that decide to cope against money laundering is also affected by number of employees that are prepared or willing to fight it. Of course, these decisions are affected by the design of optimal regulatory system made by the government which may reflect its commitment to combat money laundering.

The efficiency of the anti‐money laundering regulation may be subject to endogenous characteristics of countries that range from the regulatory design to the willingness of banks and employees to cope against money laundering.

This is a theoretical result that shows that an efficient combat to money laundering depends on the joint effort of competent authorities, banks, and employees.

To the best of the author's knowledge, the paper is the first attempt to approach money laundering combat by using an evolutionary game theory approach, which allows it to focus on the endogenous aspect of the anti‐money laundering fight.

Presents a basic model by which a representative agent chooses how to allocate his savings optimally between money of legal origin and dirty money; he holds the laundered dirty money, from which the money laundering process generates a positive transaction cost. Analyses the welfare of such an economy as a result of money laundering, also the conditions for reducing the incentives for this transforming of illegal into legal money: the incentive can be minimised if the effectiveness of anti‐money laundering increases. Concludes that the welfare of an economy where there is only legal money is greater than one in which legal and illegal activities coexist.

In this paper, a small revision on force control strategies is presented, stressing the implementation requirements of each strategy with the objective of clarifying the reasons of their poor practical utilization in industry. Furthermore, recognizing that direct force control approaches require system changes on actual robot control systems, which calls for manufacturer cooperation, an indirect force control approach is presented. The controller is introduced along with implementation description. The system is tested with an industrial deburring application that is also briefly introduced. Research experimental results of the proposed controller obtained in the laboratory, and experimental results obtained with the industrial deburring application are presented and discussed.

The purpose of this paper is to approach the issue of fraud prevention by using the theory of incentives to design a mechanism that makes employees to reveal their true type, that is, their willingness or ability to combat corruption. Based on this information, the manager may propose particular contracts for different types of employee.

The mechanism design approach assumes that the manager or the principal is entrusted with the power of making the employees agents – to provide effort. This methodology is adapted to assess the efficiency of the labor contracts to prevent fraud.

The paper shows that the labor contracts are efficient to combat fraud when the type of the employee, measured by his/her willingness to cope with fraud prevention is known by the manager. If this worker's private information not available for the manager, then the contract is not efficient due to the problem of hidden information that leads to a moral hazard. Only a second‐best solution is attained in this case.

The labor contracts are subject to the problem of hidden information.

This is a theoretical result that points out the importance of using labor contracts in order to stimulate workers to cope with fraud prevention.

The paper presents an attempt to apply an incentive theoretic approach to evaluate the efficiency of the labor contract scheme of combating fraud. Also, it reveals the fragile points of such an approach and how to overcome them.

To assess the efficiency of the anti‐money laundering regulation by using an incentive‐based approach.

It is designed a mechanism in which competent authority is the principal and the financial institutions are the agents.

The analysis shows that despite the efforts of the authorities to combat money laundering the efficiency of an incentive‐based approach has been damaged by the existence of hidden information related to the ability or willingness of banks to cope with money‐laundering prevention.

The contract approach adopted to assess the efficiency of the regulation may highlight the efficiency properties of international schemes of combat against money laundering. But, it is subject to limitations related to the hidden information.

This is a theoretical result that points out to the necessity of creating financial intelligence units in order to concentrated the efforts of collecting information about the willingness or ability of banks to cope with money laundering prevention.

The paper presents an attempt to apply an incentive theoretic approach to evaluate the efficiency of a scheme of combating money laundering. Besides, it reveals the fragile connections in the framework to combat money laundering.